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Algorithms, Volume 11, Issue 11 (November 2018)

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Open AccessArticle Vibration Suppression of a Flexible-Joint Robot Based on Parameter Identification and Fuzzy PID Control
Algorithms 2018, 11(11), 189; https://doi.org/10.3390/a11110189
Received: 31 October 2018 / Revised: 16 November 2018 / Accepted: 17 November 2018 / Published: 20 November 2018
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Abstract
In order to eliminate the influence of the joint torsional vibration on the system operation accuracy, the parameter identification and the elastic torsional vibration control of a flexible-joint robot are studied. Firstly, the flexible-joint robot system is equivalent to a rotor dynamic system,
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In order to eliminate the influence of the joint torsional vibration on the system operation accuracy, the parameter identification and the elastic torsional vibration control of a flexible-joint robot are studied. Firstly, the flexible-joint robot system is equivalent to a rotor dynamic system, in which the mass block and the torsion spring are used to simulate the system inertia link and elasticity link, for establishing the system dynamic model, and the experimental prototype is constructed. Then, based on the mechanism method, the global electromechanical-coupling dynamic model of the flexible-joint robot system is constructed to clear and define the mapping relationship between the driving voltage of the DC motor and the rotational speed of joint I and joint II. Furthermore, in view of the contradiction between the system response speed and the system overshoot in the vibration suppression effect of the conventional PID controller, a fuzzy PID controller, whose parameters are determined by the different requirements in the vibration control process, is designed to adjust the driving voltage of the DC motor for attenuating the system torsional vibration. Finally, simulation and control experiments are carried out and the results show that the designed fuzzy PID controller can effectively suppress the elastic torsional vibration of the flexible-joint robot system with synchronization optimization of control accuracy and dynamic quality. Full article
(This article belongs to the Special Issue Algorithms for PID Controller)
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Open AccessArticle Differential-Evolution-Based Coevolution Ant Colony Optimization Algorithm for Bayesian Network Structure Learning
Algorithms 2018, 11(11), 188; https://doi.org/10.3390/a11110188
Received: 17 October 2018 / Revised: 8 November 2018 / Accepted: 16 November 2018 / Published: 19 November 2018
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Abstract
Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the performance of ant colony optimization (ACO) in
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Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the performance of ant colony optimization (ACO) in learning the BNs structure, this paper proposes a new improved coevolution ACO (coACO) algorithm, which uses the pheromone information as the cooperative factor and the differential evolution (DE) as the cooperative strategy. Different from the basic ACO, the coACO divides the entire ant colony into various sub-colonies (groups), among which DE operators are adopted to implement the cooperative evolutionary process. Experimental results demonstrate that the proposed coACO outperforms the basic ACO in learning the BN structure in terms of convergence and accuracy. Full article
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Open AccessEditorial Special Issue on Reconfiguration Problems
Algorithms 2018, 11(11), 187; https://doi.org/10.3390/a11110187
Received: 12 November 2018 / Accepted: 12 November 2018 / Published: 19 November 2018
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Abstract
The study of reconfiguration problems has grown into a field of its own. The basic idea is to consider the scenario of moving from one given (feasible) solution to another, maintaining feasibility for all intermediate solutions. The solution space is often represented by
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The study of reconfiguration problems has grown into a field of its own. The basic idea is to consider the scenario of moving from one given (feasible) solution to another, maintaining feasibility for all intermediate solutions. The solution space is often represented by a “reconfiguration graph”, where vertices represent solutions to the problem in hand and an edge between two vertices means that one can be obtained from the other in one step. A typical application background would be for a reorganization or repair work that has to be done without interruption to the service that is provided. Full article
(This article belongs to the Special Issue Reconfiguration Problems)
Open AccessArticle Pricing Strategies of Logistics Distribution Services for Perishable Commodities
Algorithms 2018, 11(11), 186; https://doi.org/10.3390/a11110186
Received: 22 October 2018 / Revised: 12 November 2018 / Accepted: 14 November 2018 / Published: 17 November 2018
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Abstract
The problem of pricing distribution services is challenging due to the loss in value of product during its distribution process. Four logistics service pricing strategies are constructed in this study, including fixed pricing model, fixed pricing model with time constraints, dynamic pricing model,
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The problem of pricing distribution services is challenging due to the loss in value of product during its distribution process. Four logistics service pricing strategies are constructed in this study, including fixed pricing model, fixed pricing model with time constraints, dynamic pricing model, and dynamic pricing model with time constraints in combination with factors, such as the distribution time, customer satisfaction, optimal pricing, etc. By analyzing the relationship between optimal pricing and key parameters (such as the value of the decay index, the satisfaction of consumers, dispatch time, and the storage cost of the commodity), it is found that the larger the value of the attenuation coefficient, the easier the perishable goods become spoilage, which leads to lower distribution prices and impacts consumer satisfaction. Moreover, the analysis of the average profit of the logistics service providers in these four pricing models shows that the average profit in the dynamic pricing model with time constraints is better. Finally, a numerical experiment is given to support the findings. Full article
(This article belongs to the Special Issue Algorithms for Decision Making)
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Open AccessArticle An Algorithmic Look at Financial Volatility
Algorithms 2018, 11(11), 185; https://doi.org/10.3390/a11110185
Received: 13 September 2018 / Revised: 29 October 2018 / Accepted: 4 November 2018 / Published: 13 November 2018
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Abstract
In this paper, we attempt to give an algorithmic explanation to volatility clustering, one of the most exploited stylized facts in finance. Our analysis with daily data from five exchanges shows that financial volatilities follow Levin’s universal distribution Kirchherr et al. (1997) once
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In this paper, we attempt to give an algorithmic explanation to volatility clustering, one of the most exploited stylized facts in finance. Our analysis with daily data from five exchanges shows that financial volatilities follow Levin’s universal distribution Kirchherr et al. (1997) once transformed into equally proportional binary strings. Frequency ranking of binary trading weeks coincides with that of their Kolmogorov complexity estimated by Delahaye et al. (2012). According to Levin’s universal distribution, large (resp. small) volatilities are more likely to be followed by large (resp. small) ones since simple trading weeks such as “00000” or “11111” are much more frequently observed than complex ones such as “10100” or “01011”. Thus, volatility clusters may not be attributed to behavioral or micro-structural assumptions but to the complexity discrepancy between finite strings. This property of financial data could be at the origin of volatility autocorrelation, though autocorrelated volatilities simulated from Generalized Auto-Regressive Conditional Heteroskedacity (hereafter GARCH) cannot be transformed into universally distributed binary weeks. Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach
Algorithms 2018, 11(11), 184; https://doi.org/10.3390/a11110184
Received: 7 October 2018 / Revised: 31 October 2018 / Accepted: 31 October 2018 / Published: 8 November 2018
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Abstract
Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e.,
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Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved. Full article
(This article belongs to the Special Issue Dictionary Learning Algorithms and Applications)
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Open AccessArticle Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
Algorithms 2018, 11(11), 183; https://doi.org/10.3390/a11110183
Received: 8 October 2018 / Revised: 28 October 2018 / Accepted: 2 November 2018 / Published: 8 November 2018
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Abstract
To enhance traffic efficiency, in this paper, a novel virtual belt algorithm is proposed for the management of an isolated autonomous intersection. The proposed virtual belt algorithm consists of an offline algorithm and an online algorithm. Using the offline algorithm, the considered intersection
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To enhance traffic efficiency, in this paper, a novel virtual belt algorithm is proposed for the management of an isolated autonomous intersection. The proposed virtual belt algorithm consists of an offline algorithm and an online algorithm. Using the offline algorithm, the considered intersection can be modeled as several virtual belts. The online algorithm is designed for the real-time application of the virtual belt algorithm. Compared with the related algorithms, the main advantage of the proposed algorithm is that, there are several candidate trajectories for each approaching vehicle. Thus, there are more opportunities for an approaching vehicle to obtain a permission to pass an intersection, which is effective to improve traffic efficiency. The proposed algorithm is validated using numerical simulations conducted by Matlab and VISSIM. The simulation results show that the proposed algorithm is effective for autonomous intersection management. Full article
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Open AccessArticle An Algorithm for Interval-Valued Intuitionistic Fuzzy Preference Relations in Group Decision Making Based on Acceptability Measurement and Priority Weight Determination
Algorithms 2018, 11(11), 182; https://doi.org/10.3390/a11110182
Received: 13 October 2018 / Revised: 2 November 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
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Abstract
Group decision making with intuitionistic fuzzy preference information contains two key issues: acceptability measurement and priority weight determination. In this paper, we investigate the above two issues with respect to multiplicative interval-valued intuitionistic fuzzy preference relation (IVIFPR). Firstly, a consistency index is defined
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Group decision making with intuitionistic fuzzy preference information contains two key issues: acceptability measurement and priority weight determination. In this paper, we investigate the above two issues with respect to multiplicative interval-valued intuitionistic fuzzy preference relation (IVIFPR). Firstly, a consistency index is defined to measure the multiplicative consistency degree of IVIFPR and an optimization model is established to improve the consistency degree of IVIFPR to an acceptable one. Next, in terms of priority weight determination, an error-analysis-based extension method is proposed to obtain priority weight vector from the acceptable IVIFPR. For GDM problems, decision makers’ weights are derived by the proposed multiplicative consistency index. Subsequently, the collective IVIFPR is obtained by using an interval-valued intuitionistic fuzzy (IVIF) weighted averaging operator. Finally, a step-by step algorithm for GDM with IVIFPRs is given, and an example of enterprise innovation partner selection is analyzed, and comparative analyses with existing approaches are performed to demonstrate that the proposed algorithm is both effective and practical in dealing with GDM problems. Full article
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Open AccessArticle Measuring the Impact of Financial News and Social Media on Stock Market Modeling Using Time Series Mining Techniques
Algorithms 2018, 11(11), 181; https://doi.org/10.3390/a11110181
Received: 23 August 2018 / Revised: 17 October 2018 / Accepted: 23 October 2018 / Published: 6 November 2018
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Abstract
In this work, we study the task of predicting the closing price of the following day of a stock, based on technical analysis, news articles and public opinions. The intuition of this study lies in the fact that technical analysis contains information about
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In this work, we study the task of predicting the closing price of the following day of a stock, based on technical analysis, news articles and public opinions. The intuition of this study lies in the fact that technical analysis contains information about the event, but not the cause of the change, while data like news articles and public opinions may be interpreted as a cause. The paper uses time series analysis techniques such as Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) to study the existence of a relation between price data and textual information, either from news or social media. Pattern matching techniques from time series data are also incorporated, in order to experimentally validate potential correlations of price and textual information within given time periods. The ultimate goal is to create a forecasting model that exploits the previously discovered patterns in order to augment the forecasting accuracy. Results obtained from the experimental phase are promising. The performance of the classifier shows clear signs of improvement and robustness within the time periods where patterns between stock price and the textual information have been identified, compared to the periods where patterns did not exist. Full article
(This article belongs to the Special Issue Humanistic Data Mining: Tools and Applications)
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Open AccessArticle Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model
Algorithms 2018, 11(11), 180; https://doi.org/10.3390/a11110180
Received: 30 September 2018 / Revised: 27 October 2018 / Accepted: 31 October 2018 / Published: 6 November 2018
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Abstract
This paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vector is established based on overparameterization. Then, the identification problem
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This paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vector is established based on overparameterization. Then, the identification problem is converted to a sparse optimization problem. Based on the basis pursuit de-noising criterion and the auxiliary model identification idea, an auxiliary model based basis pursuit de-noising iterative algorithm is presented. The parameters are estimated by solving a quadratic program, and the unavailable terms in the information vector are updated by the auxiliary model outputs iteratively. The time-delays are estimated according to the sparse structure of the parameter vector. The proposed method can obtain effective estimates of the parameters and time-delays from few sampled data. The simulation results illustrate the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle A Reciprocal-Selection-Based ‘Win–Win’ Overlay Spectrum-Sharing Scheme for Device-to-Device-Enabled Cellular Network
Algorithms 2018, 11(11), 179; https://doi.org/10.3390/a11110179
Received: 11 October 2018 / Revised: 31 October 2018 / Accepted: 1 November 2018 / Published: 6 November 2018
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Abstract
This paper proposes a reciprocal-selection-based ‘Win–Win’ overlay spectrum-sharing scheme for device-to-Device-enabled cellular networks to address the resource sharing between Device-to-Device devices and the cellular users by using an overlay approach. Based on the proposed scheme, the cell edge users intend to lease part
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This paper proposes a reciprocal-selection-based ‘Win–Win’ overlay spectrum-sharing scheme for device-to-Device-enabled cellular networks to address the resource sharing between Device-to-Device devices and the cellular users by using an overlay approach. Based on the proposed scheme, the cell edge users intend to lease part of its spectrum resource to Device-to-Device transmission pairs. However, the Device-to-Device users have to provide the cooperative transmission assistance for the cell edge users in order to improve the Quality of Service of the uplink transmission from the cell edge users to the base station. Compared to the underlay spectrum-sharing scheme, overlay spectrum-sharing scheme may reduce spectrum efficiency. Hence, Non-Orthogonal Multiple Access technology is invoked at the Device-to-Device transmitter in order to improve the spectrum efficiency. The Stackelberg game is exploited to model the behaviours of the cell edge users and Device-to-Device devices. Moreover, based on matching theory, the cell edge users and Device-to-Device pairs form one-to-one matching and the stability of matching is analysed. The simulation results show that the proposed reciprocal-selection-based ‘Win–Win’ overlay spectrum-sharing scheme is capable of providing considerable rate improvements for both EUs and D2D pairs and reducing transmit power dissipated by the D2D transmitter to forward data for the EU compared with the existing methods. Full article
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Open AccessArticle Deep Directional Network for Object Tracking
Algorithms 2018, 11(11), 178; https://doi.org/10.3390/a11110178
Received: 10 October 2018 / Revised: 28 October 2018 / Accepted: 1 November 2018 / Published: 5 November 2018
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Abstract
Existing object trackers are mostly based on correlation filtering and neural network frameworks. Correlation filtering is fast but has poor accuracy. Although a neural network can achieve high precision, a large amount of computation increases the tracking time. To address this problem, we
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Existing object trackers are mostly based on correlation filtering and neural network frameworks. Correlation filtering is fast but has poor accuracy. Although a neural network can achieve high precision, a large amount of computation increases the tracking time. To address this problem, we utilize a convolutional neural network (CNN) to learn object direction. We propose a target direction classification network based on CNNs that has a directional shortcut to the tracking target, unlike the particle filter that randomly finds the target. Our network uses an end-to-end approach to determine scale variation that has good robustness to scale variation sequences. In the pretraining stage, the Visual Object Tracking Challenges (VOT) dataset is used to train the network for learning positive and negative sample classification and direction classification. In the online tracking stage, the sliding window operation is performed by using the obtained directional information to determine the exact position of the object. The network only calculates a single sample, which guarantees a low computational burden. The positive and negative sample redetection strategies can successfully ensure that the samples are not lost. The one-pass evaluation (OPE) evaluation results of the object tracking benchmark (OTB) demonstrate that the algorithm is very robust and is also faster than several deep trackers. Full article
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Open AccessArticle Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data
Algorithms 2018, 11(11), 177; https://doi.org/10.3390/a11110177
Received: 16 September 2018 / Revised: 29 October 2018 / Accepted: 29 October 2018 / Published: 4 November 2018
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Abstract
Clustering is one of the main tasks of machine learning. Internal clustering validation indexes (CVIs) are used to measure the quality of several clustered partitions to determine the local optimal clustering results in an unsupervised manner, and can act as the objective function
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Clustering is one of the main tasks of machine learning. Internal clustering validation indexes (CVIs) are used to measure the quality of several clustered partitions to determine the local optimal clustering results in an unsupervised manner, and can act as the objective function of clustering algorithms. In this paper, we first studied several well-known internal CVIs for categorical data clustering, and proved the ineffectiveness of evaluating the partitions of different numbers of clusters without any inter-cluster separation measures or assumptions; the accurateness of separation, along with its coordination with the intra-cluster compactness measures, can notably affect performance. Then, aiming to enhance the internal clustering validation measurement, we proposed a new internal CVI—clustering utility based on the averaged information gain of isolating each cluster (CUBAGE)—which measures both the compactness and the separation of the partition. The experimental results supported our findings with regard to the existing internal CVIs, and showed that the proposed CUBAGE outperforms other internal CVIs with or without a pre-known number of clusters. Full article
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Open AccessArticle Towards the Verbal Decision Analysis Paradigm for Implementable Prioritization of Software Requirements
Algorithms 2018, 11(11), 176; https://doi.org/10.3390/a11110176
Received: 18 September 2018 / Revised: 22 October 2018 / Accepted: 30 October 2018 / Published: 3 November 2018
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Abstract
The activity of prioritizing software requirements should be done as efficiently as possible. Selecting the most stable requirements for the most important customers of a development company can be a positive factor considering that available resources do not always encompass the implementation of
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The activity of prioritizing software requirements should be done as efficiently as possible. Selecting the most stable requirements for the most important customers of a development company can be a positive factor considering that available resources do not always encompass the implementation of all requirements. There are many quantitative methods for prioritization of software releases in the field of search-based software engineering (SBSE). However, we show that it is possible to use qualitative verbal decision analysis (VDA) methods to solve this type of problem. Moreover, we will use the ZAPROS III-i method to prioritize requirements considering the opinion of the decision-maker, who will participate in this process. Results obtained using VDA structured methods were found to be quite satisfactory when compared to methods using SBSE. A comparison of results between quantitative and qualitative methods will be made and discussed later. The results were reviewed and corroborated with the use of performance metrics. Full article
(This article belongs to the Special Issue Algorithms for Decision Making)
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Open AccessArticle The Bias Compensation Based Parameter and State Estimation for Observability Canonical State-Space Models with Colored Noise
Algorithms 2018, 11(11), 175; https://doi.org/10.3390/a11110175
Received: 4 September 2018 / Revised: 22 October 2018 / Accepted: 22 October 2018 / Published: 1 November 2018
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This paper develops a bias compensation-based parameter and state estimation algorithm for the observability canonical state-space system corrupted by colored noise. The state-space system is transformed into a linear regressive model by eliminating the state variables. Based on the determination of the noise
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This paper develops a bias compensation-based parameter and state estimation algorithm for the observability canonical state-space system corrupted by colored noise. The state-space system is transformed into a linear regressive model by eliminating the state variables. Based on the determination of the noise variance and noise model, a bias correction term is added into the least squares estimate, and the system parameters and states are computed interactively. The proposed algorithm can generate the unbiased parameter estimate. Two illustrative examples are given to show the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization
Algorithms 2018, 11(11), 174; https://doi.org/10.3390/a11110174
Received: 20 August 2018 / Revised: 16 October 2018 / Accepted: 29 October 2018 / Published: 1 November 2018
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Abstract
We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local
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We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local coupled parameters, are determined and optimized based on the particle swarm optimization (PSO) algorithm. Compared with extreme learning machine (ELM), LC-ELM and extreme learning machine based on particle optimization (PSO-ELM) that have the same network size or compact network configuration, simulation results in terms of regression and classification benchmark problems show that the proposed algorithm, which is called LC-PSO-ELM, has improved generalization performance and robustness. Full article
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Open AccessArticle Inapproximability of Rank, Clique, Boolean, and Maximum Induced Matching-Widths under Small Set Expansion Hypothesis
Algorithms 2018, 11(11), 173; https://doi.org/10.3390/a11110173
Received: 3 September 2018 / Revised: 23 October 2018 / Accepted: 30 October 2018 / Published: 31 October 2018
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Abstract
Wu et al. (2014) showed that under the small set expansion hypothesis (SSEH) there is no polynomial time approximation algorithm with any constant approximation factor for several graph width parameters, including tree-width, path-width, and cut-width (Wu et al. 2014). In this paper, we
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Wu et al. (2014) showed that under the small set expansion hypothesis (SSEH) there is no polynomial time approximation algorithm with any constant approximation factor for several graph width parameters, including tree-width, path-width, and cut-width (Wu et al. 2014). In this paper, we extend this line of research by exploring other graph width parameters: We obtain similar approximation hardness results under the SSEH for rank-width and maximum induced matching-width, while at the same time we show the approximation hardness of carving-width, clique-width, NLC-width, and boolean-width. We also give a simpler proof of the approximation hardness of tree-width, path-width, and cut-widththan that of Wu et al. Full article
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Open AccessArticle Bidirectional Grid Long Short-Term Memory (BiGridLSTM): A Method to Address Context-Sensitivity and Vanishing Gradient
Algorithms 2018, 11(11), 172; https://doi.org/10.3390/a11110172
Received: 10 July 2018 / Revised: 24 September 2018 / Accepted: 29 October 2018 / Published: 30 October 2018
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Abstract
The Recurrent Neural Network (RNN) utilizes dynamically changing time information through time cycles, so it is very suitable for tasks with time sequence characteristics. However, with the increase of the number of layers, the vanishing gradient occurs in the RNN. The Grid Long
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The Recurrent Neural Network (RNN) utilizes dynamically changing time information through time cycles, so it is very suitable for tasks with time sequence characteristics. However, with the increase of the number of layers, the vanishing gradient occurs in the RNN. The Grid Long Short-Term Memory (GridLSTM) recurrent neural network can alleviate this problem in two dimensions by taking advantage of the two dimensions calculated in time and depth. In addition, the time sequence task is related to the information of the current moment before and after. In this paper, we propose a method that takes into account context-sensitivity and gradient problems, namely the Bidirectional Grid Long Short-Term Memory (BiGridLSTM) recurrent neural network. This model not only takes advantage of the grid architecture, but it also captures information around the current moment. A large number of experiments on the dataset LibriSpeech show that BiGridLSTM is superior to other deep LSTM models and unidirectional LSTM models, and, when compared with GridLSTM, it gets about 26 percent gain improvement. Full article
(This article belongs to the Special Issue Deep Learning and Semantic Technologies)
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Open AccessArticle Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework
Algorithms 2018, 11(11), 171; https://doi.org/10.3390/a11110171
Received: 17 September 2018 / Revised: 22 October 2018 / Accepted: 26 October 2018 / Published: 28 October 2018
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Abstract
The Directional Changes (DC) framework is an approach to summarize price movement in financial time series. Some studies have tried to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy that has been developed based on
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The Directional Changes (DC) framework is an approach to summarize price movement in financial time series. Some studies have tried to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy that has been developed based on the DC framework. Despite the promising results of DBA, DBA employed neither an order size management nor risk management components. In this paper, we present an improved version of DBA named Intelligent DBA (IDBA). IDBA overcomes the weaknesses of DBA as it embraces an original order size management and risk management modules. We examine the performance of IDBA in the forex market. The results suggest that IDBA can provide significantly greater returns than DBA. The results also show that the IDBA outperforms another DC-based trading strategy and that it can generate annualized returns of about 30% after deducting the bid and ask spread (but not the transaction costs). Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle A Machine Learning View on Momentum and Reversal Trading
Algorithms 2018, 11(11), 170; https://doi.org/10.3390/a11110170
Received: 15 September 2018 / Revised: 18 October 2018 / Accepted: 24 October 2018 / Published: 26 October 2018
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Abstract
Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it
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Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns. Full article
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Open AccessArticle Parameter Estimation of a Class of Neural Systems with Limit Cycles
Algorithms 2018, 11(11), 169; https://doi.org/10.3390/a11110169
Received: 11 September 2018 / Revised: 22 October 2018 / Accepted: 23 October 2018 / Published: 26 October 2018
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This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursive least-squares and stochastic gradient algorithms including their
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This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursive least-squares and stochastic gradient algorithms including their multi-innovation versions by introducing an innovation vector are proposed. The simulation results of the FitzHugh–Nagumo model indicate that the proposed algorithms perform according to the expected effectiveness. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle Fractional Order Sliding Mode Control of a Class of Second Order Perturbed Nonlinear Systems: Application to the Trajectory Tracking of a Quadrotor
Algorithms 2018, 11(11), 168; https://doi.org/10.3390/a11110168
Received: 24 June 2018 / Revised: 11 October 2018 / Accepted: 22 October 2018 / Published: 26 October 2018
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Abstract
A Fractional Order Sliding Mode Control (FOSMC) is proposed in this paper for an integer second order nonlinear system with an unknown additive perturbation term. A sufficient condition is given to assure the attractiveness to a given sliding surface where trajectory tracking is
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A Fractional Order Sliding Mode Control (FOSMC) is proposed in this paper for an integer second order nonlinear system with an unknown additive perturbation term. A sufficient condition is given to assure the attractiveness to a given sliding surface where trajectory tracking is assured, despite the presence of the perturbation term. The control scheme is applied to the model of a quadrotor vehicle in order to have trajectory tracking in the space. Simulation results are presented to evaluate the performance of the control scheme. Full article
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Open AccessArticle Online Adaptive Parameter Estimation for Quadrotors
Algorithms 2018, 11(11), 167; https://doi.org/10.3390/a11110167
Received: 11 September 2018 / Revised: 19 October 2018 / Accepted: 23 October 2018 / Published: 25 October 2018
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Abstract
The stability and robustness of quadrotors are always influenced by unknown or immeasurable system parameters. This paper proposes a novel adaptive parameter estimation technology to obtain high-accuracy parameter estimation for quadrotors. A typical mathematical model of quadrotors is first obtained, which can be
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The stability and robustness of quadrotors are always influenced by unknown or immeasurable system parameters. This paper proposes a novel adaptive parameter estimation technology to obtain high-accuracy parameter estimation for quadrotors. A typical mathematical model of quadrotors is first obtained, which can be used for parameter estimation. Then, an expression of the parameter estimation error is derived by introducing a set of auxiliary filtered variables. Moreover, an augmented matrix is constructed based on the obtained auxiliary filtered variables, which is then used to design new adaptive laws to achieve exponential convergence under the standard persistent excitation (PE) condition. Finally, a simulation and an experimental verification for a typical quadrotor system are shown to illustrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Open AccessArticle High-Gain Observer-Based Sliding-Mode Dynamic Surface Control for Particleboard Glue Mixing and Dosing System
Algorithms 2018, 11(11), 166; https://doi.org/10.3390/a11110166
Received: 10 October 2018 / Revised: 19 October 2018 / Accepted: 21 October 2018 / Published: 23 October 2018
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Abstract
In the process of particleboard glue mixing and dosing control under the working condition of intermediate frequency, a sliding-mode dynamic surface control strategy based on high-gain observer is proposed in this paper to deal with the problem of glue flow stability caused by
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In the process of particleboard glue mixing and dosing control under the working condition of intermediate frequency, a sliding-mode dynamic surface control strategy based on high-gain observer is proposed in this paper to deal with the problem of glue flow stability caused by strong nonlinearity. The high-gain observer (HGO) is aimed at estimating the derivative of the immeasurable system input signal for feedback, and the robustness of the system is improved by the dynamic surface control (DSC) method. Furthermore, the sliding-mode control (SMC) method is used to deal with disturbances caused by the uncertainties as well as external disturbances. It is proven that the system is exponential asymptotic stable by constructing a suitable Lyapunov function. Simulation results show that the proposed control methods can make the system track the expected flow value quickly and accurately. Finally, numerical simulation results are exhibited to authenticate and validate the effectiveness of the proposed control scheme. Full article
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Open AccessArticle Efficient Algorithms for Computing the Inner Edit Distance of a Regular Language via Transducers
Algorithms 2018, 11(11), 165; https://doi.org/10.3390/a11110165
Received: 6 October 2018 / Revised: 16 October 2018 / Accepted: 18 October 2018 / Published: 23 October 2018
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Abstract
The concept of edit distance and its variants has applications in many areas such as computational linguistics, bioinformatics, and synchronization error detection in data communications. Here, we revisit the problem of computing the inner edit distance of a regular language given via a
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The concept of edit distance and its variants has applications in many areas such as computational linguistics, bioinformatics, and synchronization error detection in data communications. Here, we revisit the problem of computing the inner edit distance of a regular language given via a Nondeterministic Finite Automaton (NFA). This problem relates to the inherent maximal error-detecting capability of the language in question. We present two efficient algorithms for solving this problem, both of which execute in time O ( r 2 n 2 d ) , where r is the cardinality of the alphabet involved, n is the number of transitions in the given NFA, and d is the computed edit distance. We have implemented one of the two algorithms and present here a set of performance tests. The correctness of the algorithms is based on the connection between word distances and error detection and the fact that nondeterministic transducers can be used to represent the errors (resp., edit operations) involved in error-detection (resp., in word distances). Full article
(This article belongs to the Special Issue String Matching and Its Applications)
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