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Keywords = fuzzy broad learning system

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22 pages, 1847 KiB  
Article
Unveiling Hidden Dynamics in Air Traffic Networks: An Additional-Symmetry-Inspired Framework for Flight Delay Prediction
by Chao Yin, Xinke Du, Jianyu Duan, Qiang Tang and Li Shen
Mathematics 2025, 13(14), 2274; https://doi.org/10.3390/math13142274 - 15 Jul 2025
Viewed by 172
Abstract
Flight delays pose a significant challenge to the modern aviation industry, with prediction difficulties arising from the need to accurately model spatio-temporal dependencies and uncertainties within complex air traffic networks. To address this challenge, this study proposes a novel hybrid predictive framework named [...] Read more.
Flight delays pose a significant challenge to the modern aviation industry, with prediction difficulties arising from the need to accurately model spatio-temporal dependencies and uncertainties within complex air traffic networks. To address this challenge, this study proposes a novel hybrid predictive framework named DenseNet-LSTM-FBLS. The framework first employs a DenseNet-LSTM module for deep spatio-temporal feature extraction, where DenseNet captures the intricate spatial correlations between airports, and LSTM models the temporal evolution of delays and meteorological conditions. In a key innovation, the extracted features are fed into a Fuzzy Broad Learning System (FBLS)—marking the first application of this method in the field of flight delay prediction. The FBLS component effectively handles data uncertainty through its fuzzy logic, while its “broad” architecture offers greater computational efficiency compared to traditional deep networks. Validated on a large-scale dataset of 198,970 real-world European flights, the proposed model achieves a prediction accuracy of 92.71%, significantly outperforming various baseline models. The results demonstrate that the DenseNet-LSTM-FBLS framework provides a highly accurate and efficient solution for flight delay forecasting, highlighting the considerable potential of Fuzzy Broad Learning Systems for tackling complex real-world prediction tasks. Full article
(This article belongs to the Special Issue Symmetries of Integrable Systems, 2nd Edition)
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20 pages, 4851 KiB  
Article
Research on a Network Diagnosis Method for a Train Control Center and Interlocking Integrated System Based on a Fuzzy Broad Learning System Model
by Lei Yuan, Yinghui Li, Guodong Wei and Wenzhang Guo
Electronics 2025, 14(4), 691; https://doi.org/10.3390/electronics14040691 - 10 Feb 2025
Viewed by 533
Abstract
In high-speed railway signaling systems, the network structure of the Train Control Center and Inter-locking Integrated System (TIS) is highly complex, with a large number of interfaces, numerous redundant channels, and forwarding components such as switches. These factors result in challenges such as [...] Read more.
In high-speed railway signaling systems, the network structure of the Train Control Center and Inter-locking Integrated System (TIS) is highly complex, with a large number of interfaces, numerous redundant channels, and forwarding components such as switches. These factors result in challenges such as insufficient accuracy, low efficiency, and poor real-time performance in terms of network monitoring and fault diagnosis. As the scale of railway yards continues to expand, these issues are becoming increasingly prominent. To address these challenges, this paper proposes a network fault propagation model based on the Fuzzy Broad Learning System (FBLS). By leveraging nonlinear transformations and feature mapping techniques, FBLS can efficiently extract and analyze network fault features, even with a relatively small amount of data. Experimental results show that the FBLS-based diagnostic model achieves higher accuracy and faster response speed in fault identification and propagation path analysis compared to traditional graph theory and fuzzy reasoning methods. Further comparisons with existing methods validate the advantages of FBLS in handling multi-source data, improving noise tolerance, and adapting to large-scale railway yard network systems, demonstrating its broad application prospects in railway signaling systems. Full article
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23 pages, 4126 KiB  
Article
Furnace Temperature Model Predictive Control Based on Particle Swarm Rolling Optimization for Municipal Solid Waste Incineration
by Hao Tian, Jian Tang and Tianzheng Wang
Sustainability 2024, 16(17), 7670; https://doi.org/10.3390/su16177670 - 4 Sep 2024
Cited by 2 | Viewed by 1700
Abstract
Precise control of furnace temperature (FT) is crucial for the stable, efficient operation and pollution control of the municipal solid waste incineration (MSWI) process. To address the inherent nonlinearity and uncertainty of the incineration process, a FT control strategy is proposed. Firstly, by [...] Read more.
Precise control of furnace temperature (FT) is crucial for the stable, efficient operation and pollution control of the municipal solid waste incineration (MSWI) process. To address the inherent nonlinearity and uncertainty of the incineration process, a FT control strategy is proposed. Firstly, by analyzing the process characteristics of the MSWI process in terms of FT control, the secondary air flow is selected as the manipulated variable to control the FT. Secondly, an FT prediction model based on the Interval Type-2 Fuzzy Broad Learning System (IT2FBLS) is developed, incorporating online parameter learning and structural learning algorithms to enhance prediction accuracy. Next, particle swarm rolling optimization (PSRO) is used to solve the optimal control law sequence to ensure optimization efficiency. Finally, the stability of the proposed method is validated using Lyapunov theory, confirming the controller’s reliability in practical applications. Experiments based on actual operational data confirm the method’s effectiveness. Full article
(This article belongs to the Special Issue AI Application in Sustainable MSWI Process)
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28 pages, 685 KiB  
Article
Towards Refined Autism Screening: A Fuzzy Logic Approach with a Focus on Subtle Diagnostic Challenges
by Philip Smith and Sarah Greenfield
Mathematics 2024, 12(13), 2012; https://doi.org/10.3390/math12132012 - 28 Jun 2024
Cited by 2 | Viewed by 1616
Abstract
This study explores the creation and testing of a Fuzzy Inferencing System for automating preliminary referrals for autism diagnosis, utilizing membership functions aligned with the Autism Quotient 10-item questionnaire. Validated across three distinct datasets, the system demonstrated perfect accuracy in deterministic settings and [...] Read more.
This study explores the creation and testing of a Fuzzy Inferencing System for automating preliminary referrals for autism diagnosis, utilizing membership functions aligned with the Autism Quotient 10-item questionnaire. Validated across three distinct datasets, the system demonstrated perfect accuracy in deterministic settings and an overall accuracy of 92.91% in a broad fuzzy dataset. The use of Fuzzy Logic reflects the complex and variable nature of autism diagnosis, suggesting its potential applicability in this field. While the system effectively categorized clear referral and non-referral scenarios, it faced challenges in accurately identifying cases requiring a second opinion. These results indicate the need for further refinement to enhance the efficiency and accuracy of preliminary autism screenings, pointing to future avenues for improving the system’s performance. The motivation behind this study is to address the diagnostic gap for high-functioning adults whose symptoms present in a more neurotypical manner. Many current deep learning approaches for diagnosing autism focus on quantitative datasets like fMRI and facial expressions, often overlooking behavioral traits. However, autism diagnosis still heavily relies on long histories and multi-stakeholder information from parents, teachers, doctors and behavioral experts. This research addresses the challenge of creating an automated system that can handle the nuances and variability inherent in ASD symptoms. The theoretical innovation lies in the novel application of Fuzzy Logic to interpret these subtle diagnostic indicators, providing a more systematic approach compared to traditional methods. By bridging the gap between subjective clinical evaluations and objective computational techniques, this study aims to enhance the preliminary screening process for ASD. Full article
(This article belongs to the Special Issue The Recent Advances in Computational Intelligence)
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16 pages, 2064 KiB  
Article
Research on a Multi-Dimensional Indicator Assessment Model for Evaluating Landslide Risk near Large Alpine Reservoirs
by Hanyin Hu, Hu Ke, Xinyao Zhang and Jianbo Yi
Appl. Sci. 2024, 14(12), 5201; https://doi.org/10.3390/app14125201 - 14 Jun 2024
Viewed by 1179
Abstract
Geological disasters in large alpine reservoirs primarily take the form of landslide occurrences and are predominantly induced by slope instability. Presently, risk monitoring and assessment strategies tend to prioritize sudden alerts overlooking progressive trajectories from the onset of creeping deformations within the slope [...] Read more.
Geological disasters in large alpine reservoirs primarily take the form of landslide occurrences and are predominantly induced by slope instability. Presently, risk monitoring and assessment strategies tend to prioritize sudden alerts overlooking progressive trajectories from the onset of creeping deformations within the slope to its critical state preceding landslides. Hence, analyzing landslide safety risks over time demonstrates a significant degree of hysteresis, highlighting the necessity for a comprehensive approach to risk assessment that encompasses both gradual and sudden precursors to landslide events. This study analyzes the factors affecting slope stability and establishes a slope evaluation indicator system that includes terrain morphology, meteorological conditions, the ecological environment, soil conditions, human activity, and external manifestation. It proposes a quantitative model for slope landslide risk assessment based on a fuzzy broad learning system, aiming to accurately assess slopes with different risk levels. The overall assessment accuracy rate reaches 92.08%. This multi-dimensional risk assessment model provides long-term monitoring of slope conditions and scientific guidance on landslide risk management and disaster prevention and mitigation on a long time scale for risky slopes in reservoir areas. Full article
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19 pages, 4088 KiB  
Article
Bearing Fault Diagnosis Method Based on Multi-Domain Feature Selection and the Fuzzy Broad Learning System
by Le Wu, Chao Zhang, Feifan Qin, Hongbo Fei, Guiyi Liu, Jing Zhang and Shuai Xu
Processes 2024, 12(2), 369; https://doi.org/10.3390/pr12020369 - 10 Feb 2024
Cited by 5 | Viewed by 1853
Abstract
In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of BLS, Prof. C. L. Philip Chen’s team introduced [...] Read more.
In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of BLS, Prof. C. L. Philip Chen’s team introduced the Fuzzy Broad Learning System (FBLS) by replacing the feature nodes of BLS with fuzzy subsystems, thereby further reducing the training time. However, the traditional FBLS, with its straightforward structure, falls short in achieving higher fault diagnosis accuracy when handling raw vibration signals. This paper presents a bearing fault diagnosis approach employing multi-domain feature selection and the fuzzy broad learning system (MS-FBLS), aiming to enhance the diagnostic accuracy of FBLS through multi-domain feature selection. Primarily, a set of 49 features spanning time domain, frequency domain, time-frequency domain, and entropy values is extracted from the original vibrational signals. This combination builds a 49-dimensional multidomain feature set that exploits the information behind the input data as much as possible, thus compensating for the lack of feature extraction capability in FBLS. Afterward, the Random Forest algorithm assesses the significance of all features, leading to a reordering of the multidomain feature set based on their respective importance levels. Ultimately, the reorganized multidomain feature set is then fed into the FBLS, enabling the identification of various failure states within the bearing. The experimental validation conducted on the rolling bearing fault simulation test bed showcased that, in comparison to the traditional FBLS, the MS-FBLS method not only elevates diagnostic accuracy by 23.46%, but also substantially enhances diagnostic speed. These results serve as comprehensive evidence affirming the effectiveness of the method. Full article
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18 pages, 29415 KiB  
Article
GLCM-Based FBLS: A Novel Broad Learning System for Knee Osteopenia and Osteoprosis Screening in Athletes
by Zhangtianyi Chen, Haotian Zheng, Junwei Duan and Xiangjie Wang
Appl. Sci. 2023, 13(20), 11150; https://doi.org/10.3390/app132011150 - 10 Oct 2023
Cited by 2 | Viewed by 1993
Abstract
Due to the physical strain experienced during intense workouts, athletes are at a heightened risk of developing osteopenia and osteoporosis. These conditions not only impact their overall health but also their athletic performance. The current clinical screening methods for osteoporosis are limited by [...] Read more.
Due to the physical strain experienced during intense workouts, athletes are at a heightened risk of developing osteopenia and osteoporosis. These conditions not only impact their overall health but also their athletic performance. The current clinical screening methods for osteoporosis are limited by their high radiation dose, complex post-processing requirements, and the significant time and resources needed for implementation. This makes it challenging to incorporate them into athletes’ daily training routines. Consequently, our objective was to develop an innovative automated screening approach for detecting osteopenia and osteoporosis using X-ray image data. Although several automated screening methods based on deep learning have achieved notable results, they often suffer from overfitting and inadequate datasets. To address these limitations, we proposed a novel model called the GLCM-based fuzzy broad learning system (GLCM-based FBLS). Initially, texture features of X-ray images were extracted using the gray-level co-occurrence matrix (GLCM). Subsequently, these features were combined with the fuzzy broad learning system to extract crucial information and enhance the accuracy of predicting osteoporotic conditions. Finally, we applied the proposed method to the field of osteopenia and osteoporosis screening. By comparing this model with three advanced deep learning models, we have verified the effectiveness of GLCM-based FBLS in the automatic screening of osteoporosis for athletes. Full article
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20 pages, 3445 KiB  
Article
A New Method for Commercial-Scale Water Purification Selection Using Linguistic Neural Networks
by Saleem Abdullah, Alaa O. Almagrabi and Nawab Ali
Mathematics 2023, 11(13), 2972; https://doi.org/10.3390/math11132972 - 3 Jul 2023
Cited by 10 | Viewed by 1503
Abstract
A neural network is a very useful tool in artificial intelligence (AI) that can also be referred to as an ANN. An artificial neural network (ANN) is a deep learning model that has a broad range of applications in real life. The combination [...] Read more.
A neural network is a very useful tool in artificial intelligence (AI) that can also be referred to as an ANN. An artificial neural network (ANN) is a deep learning model that has a broad range of applications in real life. The combination and interrelationship of neurons and nodes with each other facilitate the transmission of information. An ANN has a feed-forward neural network. The neurons are arranged in layers, and each layer performs a particular calculation on the incoming data. Up until the output layer, which generates the network’s ultimate output, is reached, each layer’s output is transmitted as an input to the subsequent layer. A feed-forward neural network (FFNN) is a method for finding the output of expert information. In this research, we expand upon the concept of fuzzy neural network systems and introduce feed-forward double-hierarchy linguistic neural network systems (FFDHLNNS) using Yager–Dombi aggregation operators. We also discuss the desirable properties of Yager–Dombi aggregation operators. Moreover, we describe double-hierarchy linguistic term sets (DHLTSs) and discuss the score function of DHLTSs and the distance between any two double-hierarchy linguistic term elements (DHLTEs). Here, we discuss different approaches to choosing a novel water purification technique on a commercial scale, as well as some variables influencing these approaches. We apply a feed-forward double-hierarchy linguistic neural network (FFDHLNN) to select the best method for water purification. Moreover, we use the extended version of the Technique for Order Preference by Similarity to Ideal Solution (extended TOPSIS) method and the grey relational analysis (GRA) method for the verification of our suggested approach. Remarkably, both approaches yield almost the same results as those obtained using our proposed method. The proposed models were compared with other existing models of decision support systems, and the comparison demonstrated that the proposed models are feasible and valid decision support systems. The proposed technique is more reliable and accurate for the selection of large-scale water purification methods. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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21 pages, 4282 KiB  
Article
Comprehensive Evaluation of Marine Ship Fires Risk Based on Fuzzy Broad Learning System
by Chuang Zhang, Xiaofan Zhang, Songtao Liu and Muzhuang Guo
J. Mar. Sci. Eng. 2023, 11(7), 1276; https://doi.org/10.3390/jmse11071276 - 23 Jun 2023
Cited by 4 | Viewed by 2423
Abstract
Ship fires exhibit the main characteristics of a high possibility of occurrence, large load, fast spreading, high difficulty in extinguishing, and serious losses. Therefore, once a fire occurs, it will cause huge loss in terms of economic and personnel safety. Firstly, a ship [...] Read more.
Ship fires exhibit the main characteristics of a high possibility of occurrence, large load, fast spreading, high difficulty in extinguishing, and serious losses. Therefore, once a fire occurs, it will cause huge loss in terms of economic and personnel safety. Firstly, a ship fire risk evaluation indicator system was constructed based on the causes and severity of the fires. Secondly, a comprehensive evaluation method for the fuzzy broad learning system (FBLS) was proposed. The fuzzy system was used to implement feature mapping on the input data, and the extracted fuzzy features were further input into the BLS enhancement layer. A fuzzy broad learning neural network structure was constructed by combining fuzzy features, feature nodes, and enhancement nodes. The method was applied to the field of risk assessment for the first time, and is a reference for subsequent studies. Finally, the risk levels of ship fires were classified and compared with evaluation methods such as fuzzy support vector machine (FSVM) and Fuzzy BP neural network (FBPNN) to demonstrate effectiveness and accuracy. The proposed FBLS method was used to predict actual cases, and the results showed consistency with the level determined by the accident investigation report published by the Maritime Bureau Administration. Full article
(This article belongs to the Section Marine Pollution)
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11 pages, 2575 KiB  
Article
A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System
by Xiaojia Wang, Cunjia Wang, Keyu Zhu and Xibin Zhao
Symmetry 2023, 15(1), 83; https://doi.org/10.3390/sym15010083 - 28 Dec 2022
Cited by 8 | Viewed by 1867
Abstract
In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical [...] Read more.
In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements. Full article
(This article belongs to the Special Issue New Approaches for System Identification Problems)
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15 pages, 6325 KiB  
Article
Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings
by Jianmin Zhou, Xiaotong Yang, Lulu Liu, Yunqing Wang, Junjie Wang and Guanghao Hou
Machines 2022, 10(12), 1229; https://doi.org/10.3390/machines10121229 - 16 Dec 2022
Cited by 4 | Viewed by 2310
Abstract
Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the [...] Read more.
Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-depth information of fault signals, thus achieving high fault diagnosis accuracy. However, due to the complex deep structure of deep learning, most deep learning methods require more time and resources for bearing fault diagnosis. This paper proposes a bearing fault diagnosis method combining feature engineering and fuzzy broad learning. First, time domain, frequency domain, and time-frequency domain features are extracted from the bearing signals. Then the stability and robustness indexes of these features are evaluated to complete the feature engineering. The features obtained by feature engineering are used as the input of the fault diagnosis model, and three sets of experimental data validate the model. The experimental results show that the proposed method can achieve the bearing fault diagnosis accuracy of 96.43% on the experimental bench data, 100% on the Case Western Reserve University dataset, and 100% on the centrifugal pump bearing fault dataset, with a time of approximately 0.28 s. The results show that this method has the advantages of accuracy, rapidity, and stability of bearing fault diagnosis. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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24 pages, 3254 KiB  
Review
Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications
by Manisha Singh, Gurubasavaraj Veeranna Pujar, Sethu Arun Kumar, Meduri Bhagyalalitha, Handattu Shankaranarayana Akshatha, Belal Abuhaija, Anas Ratib Alsoud, Laith Abualigah, Narasimha M. Beeraka and Amir H. Gandomi
Electronics 2022, 11(17), 2634; https://doi.org/10.3390/electronics11172634 - 23 Aug 2022
Cited by 63 | Viewed by 13579
Abstract
Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for [...] Read more.
Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis. Full article
(This article belongs to the Special Issue Big Data Analytics Using Artificial Intelligence)
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16 pages, 5774 KiB  
Article
FBLS-Based Fusion Method for Unmanned Surface Vessel Positioning Considering Denoising Algorithm
by Qifu Wang, Songtao Liu, Bingyan Zhang and Chuang Zhang
J. Mar. Sci. Eng. 2022, 10(7), 905; https://doi.org/10.3390/jmse10070905 - 30 Jun 2022
Cited by 7 | Viewed by 1772
Abstract
Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS [...] Read more.
Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS and the instability derived from the GPS signal blockage represent a potential threat to the INS/GPS integration of USV. Under this background, a composition framework based on nonlinear generalization capability of support vector machines (SVM) and multi-resolution ability of wavelet transform is used to solve the difficulty that the INS suffers from the interference of stochastic errors, and the dynamic information of the USV is not influenced. An innovative fuzzy broad learning structure based on the broad learning (BL) method is utilized in the INS/GPS integration of USV, in which the navigation information of INS and GPS are deemed as the input of the Fuzzy Broad Learning System (FBLS) to train the network, and then the trained network of FBLS and navigation information of INS are applied for estimating the optimal navigation solution during the GPS signal blockage. Based on the USV platform, a sea trial was carried out to confirm the validity and feasibility of the proposed method by comparing with existing algorithms for INS/GPS integration. The experimental results show that the proposed approach could achieve the better denoising effect from random errors of INS and provide high-accuracy navigation solutions during GPS signal blockage. Full article
(This article belongs to the Section Ocean Engineering)
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17 pages, 2227 KiB  
Article
Machine Learning for the Estimation of Diameter Increment in Mixed and Uneven-Aged Forests
by Abotaleb Salehnasab, Mahmoud Bayat, Manouchehr Namiranian, Bagher Khaleghi, Mahmoud Omid, Hafiz Umair Masood Awan, Nadir Al-Ansari and Abolfazl Jaafari
Sustainability 2022, 14(6), 3386; https://doi.org/10.3390/su14063386 - 14 Mar 2022
Cited by 14 | Viewed by 2917
Abstract
Estimating the diameter increment of forests is one of the most important relationships in forest management and planning. The aim of this study was to provide insight into the application of two machine learning methods, i.e., the multilayer perceptron artificial neural network (MLP) [...] Read more.
Estimating the diameter increment of forests is one of the most important relationships in forest management and planning. The aim of this study was to provide insight into the application of two machine learning methods, i.e., the multilayer perceptron artificial neural network (MLP) and adaptive neuro-fuzzy inference system (ANFIS), for developing diameter increment models for the Hyrcanian forests. For this purpose, the diameters at breast height (DBH) of seven tree species were recorded during two inventory periods. The trees were divided into four broad species groups, including beech (Fagus orientalis), chestnut-leaved oak (Quercus castaneifolia), hornbeam (Carpinus betulus), and other species. For each group, a separate model was developed. The k-fold strategy was used to evaluate these models. The Pearson correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were utilized to evaluate the models. RMSE and R2 of the MLP and ANFIS models were estimated for the four groups of beech ((1.61 and 0.23) and (1.57 and 0.26)), hornbeam ((1.42 and 0.13) and (1.49 and 0.10)), chestnut-leaved oak ((1.55 and 0.28) and (1.47 and 0.39)), and other species ((1.44 and 0.32) and (1.5 and 0.24)), respectively. Despite the low coefficient of determination, the correlation test in both techniques was significant at a 0.01 level for all four groups. In this study, we also determined optimal network parameters such as number of nodes of one or multiple hidden layers and the type of membership functions for modeling the diameter increment in the Hyrcanian forests. Comparison of the results of the two techniques showed that for the groups of beech and chestnut-leaved oak, the ANFIS technique performed better and that the modeling techniques have a deep relationship with the nature of the tree species. Full article
(This article belongs to the Special Issue Sustainable Forest Management and Natural Hazards Prevention)
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28 pages, 9995 KiB  
Article
Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP
by Afnan Nafees, Muhammad Faisal Javed, Sherbaz Khan, Kashif Nazir, Furqan Farooq, Fahid Aslam, Muhammad Ali Musarat and Nikolai Ivanovich Vatin
Materials 2021, 14(24), 7531; https://doi.org/10.3390/ma14247531 - 8 Dec 2021
Cited by 130 | Viewed by 6119
Abstract
Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased [...] Read more.
Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases’ features to promote the usage of green concrete. Full article
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