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Theory and Applications of Fuzzy Systems and Neural Networks

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 37710

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Dr. Alexander Gegov

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Guest Editor

School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: artificial intelligence; machine learning methods using fuzzy systems; neural networks and evolutionary algorithms
Special Issues, Collections and Topics in MDPI journals

Dr. Raheleh Jafari

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Guest Editor

School of Design, University of Leeds, Leeds LS2 9JT, UK
Interests: artificial intelligence; fuzzy logic; robotics and automation; fashion design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fuzzy systems and neural networks are the main theoretical approaches in computational intelligence. These approaches have been successfully applied in a wide range of fields such as information science, mathematics, control engineering, image processing, pattern recognition, robotics, mechatronics, consumer electronics, and systems optimisation. They provide an effective tool for data and knowledge-based modelling as well as dealing with many real-world problems with quantitative and qualitative complexity in terms of dimensionality and uncertainty.

Fuzzy systems and neural networks complement each other very well and can also be combined with other computational and artificial intelligence-based techniques such as evolutionary algorithms and machine learning to solve complex real-world problems. The integration of fuzzy systems and neural networks, in particular, can bring out the best of both approaches and usually provides better system performance in terms of modelling efficiency and accuracy.

This Special Issue aims to publish original research of the highest scientific quality related to the theory and applications of fuzzy systems and neural networks. We invite original and unpublished submissions that feature innovative methods for enhancing fuzzy systems and neural networks. The scope includes theoretical and experimental studies that contribute to novel developments in fundamental research and its applications.

Potential topics for submissions include but are not limited to:

Fuzzy systems for modelling and simulation
Neural networks for modelling and simulation
Fuzzy systems for classification and recognition
Neural networks for classification and recognition
Neuro fuzzy systems
Fuzzy neural networks
PD fuzzy and neural control
PID fuzzy and neural control
Model based fuzzy and neural control

The technical program committee members are as follows:

1. Dr. Sina Razvarz

Departamento de Control Automatico, CINVESTAV-IPN (National Polytechnic Institute), Mexico City 07360, Mexico

2. Dr. Fazad Arabikhan

School of Computing, University of Portsmouth, Portsmouth P05 3JT, UK

Dr. Alexander Gegov
Dr. Raheleh Jafari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

fuzzy systems
neural networks
intelligent systems
computational intelligence
artificial intelligence
machine learning

Related Special Issue

Theory and Applications of Fuzzy Systems and Neural Networks, Volume II in Electronics

Published Papers (12 papers)

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Research

Jump to: Review

23 pages, 5800 KiB

Open AccessArticle

Grey Wolf Optimizer in Design Process of the Recurrent Wavelet Neural Controller Applied for Two-Mass System

by Mateusz Zychlewicz, Radoslaw Stanislawski and Marcin Kaminski

Electronics 2022, 11(2), 177; https://doi.org/10.3390/electronics11020177 - 7 Jan 2022

Cited by 14 | Viewed by 1511

Abstract

In this paper, an adaptive speed controller of the electrical drive is presented. The main part of the control structure is based on the Recurrent Wavelet Neural Network (RWNN). The mechanical part of the plant is considered as an elastic connection of two DC machines. Oscillation damping and robustness against parameter changes are achieved using network parameters updates (online). Moreover, the various combinations of the feedbacks from the state variables are considered. The initial weights of the neural network and the additional gains are tuned using a modified version of the Grey Wolf Optimizer. Convergence of the calculation is forced using a new definition. For theoretical analysis, numerical tests are presented. Then, the RWNN is implemented in a dSPACE card. Finally, the simulation results are verified experimentally. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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14 pages, 3858 KiB

Open AccessArticle

Pipeline Leak Detection and Estimation Using Fuzzy PID Observer

by Raheleh Jafari, Sina Razvarz, Cristóbal Vargas-Jarillo, Alexander Gegov and Farzad Arabikhan

Electronics 2022, 11(1), 152; https://doi.org/10.3390/electronics11010152 - 4 Jan 2022

Cited by 2 | Viewed by 1696

Abstract

A pipe is a ubiquitous product in the industries that is used to convey liquids, gases, or solids suspended in a liquid, e.g., a slurry, from one location to another. Both internal and external cracking can result in structural failure of the industrial piping system and possibly decrease the service life of the equipment. The chaos and complexity associated with the uncertain behaviour inherent in pipeline systems lead to difficulty in detection and localisation of leaks in real time. The timely detection of leakage is important in order to reduce the loss rate and serious environmental consequences. The objective of this paper is to propose a new leak detection method based on an autoregressive with exogenous input (ARX) Laguerre fuzzy proportional-integral-derivative (PID) observation system. The objective of this paper is to propose a new leak detection method based on an autoregressive with exogenous input (ARX) Laguerre fuzzy proportional-integral-derivative (PID) observation system. In this work, the ARX–Laguerre model has been used to generate better performance in the presence of uncertainty. According to the results, the proposed technique can detect leaks accurately and effectively. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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28 pages, 4668 KiB

Open AccessArticle

Benchmarking GHG Emissions Forecasting Models for Global Climate Policy

by Cristiana Tudor and Robert Sova

Electronics 2021, 10(24), 3149; https://doi.org/10.3390/electronics10243149 - 17 Dec 2021

Cited by 18 | Viewed by 3145

Abstract

Climate change and pollution fighting have become prominent global concerns in the twenty-first century. In this context, accurate estimates for polluting emissions and their evolution are critical for robust policy-making processes and ultimately for solving stringent global climate challenges. As such, the primary objective of this study is to produce more accurate forecasts of greenhouse gas (GHG) emissions. This in turn contributes to the timely evaluation of the progress achieved towards meeting global climate goals set by international agendas and also acts as an early-warning system. We forecast the evolution of GHG emissions in 12 top polluting economies by using data for the 1970–2018 period and employing six econometric and machine-learning models (the exponential smoothing state-space model (ETS), the Holt–Winters model (HW), the TBATS model, the ARIMA model, the structural time series model (STS), and the neural network autoregression model (NNAR)), along with a naive model. A battery of robustness checks is performed. Results confirm a priori expectations and consistently indicate that the neural network autoregression model (NNAR) presents the best out-of-sample forecasting performance for GHG emissions at different forecasting horizons by reporting the lowest average RMSE (root mean square error) and MASE (mean absolute scaled error) within the array of predictive models. Predictions made by the NNAR model for the year 2030 indicate that total GHG emissions are projected to increase by 3.67% on average among the world’s 12 most polluting countries until 2030. Only four top polluters will record decreases in total GHG emissions values in the coming decades (i.e., Canada, the Russian Federation, the US, and China), although their emission levels will remain in the upper decile. Emission increases in a handful of developing economies will see significant growth rates (a 22.75% increase in GHG total emissions in Brazil, a 15.75% increase in Indonesia, and 7.45% in India) that are expected to offset the modest decreases in GHG emissions projected for the four countries. Our findings, therefore, suggest that the world’s top polluters cannot meet assumed pollution reduction targets in the form of NDCs under the Paris agreement. Results thus highlight the necessity for more impactful policies and measures to bring the set targets within reach. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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17 pages, 1087 KiB

Open AccessArticle

A Wavenet-Based Virtual Sensor for PM₁₀ Monitoring

by Claudio Carnevale, Enrico Turrini, Roberta Zeziola, Elena De Angelis and Marialuisa Volta

Electronics 2021, 10(17), 2111; https://doi.org/10.3390/electronics10172111 - 30 Aug 2021

Cited by 4 | Viewed by 1637

Abstract

In this work, a virtual sensor for

P M_{10}

concentration monitoring is presented. The sensor is based on wavenet models and uses daily mean

N O_{2}

concentration and meteorological variables (wind speed and rainfall) as input. The methodology has been applied [...] Read more.

In this work, a virtual sensor for

P M_{10}

concentration monitoring is presented. The sensor is based on wavenet models and uses daily mean

N O_{2}

concentration and meteorological variables (wind speed and rainfall) as input. The methodology has been applied to the reconstruction of

P M_{10}

levels measured from 14 monitoring stations in Lombardy region (Italy). This region, usually affected by high levels of

P M_{10}

, is a challenging benchmarking area for the implemented sensors. Neverthless, the performances are good with relatively low bias and high correlation. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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15 pages, 1972 KiB

Open AccessArticle

Dimensioning an FPGA for Real-Time Implementation of State of the Art Neural Network-Based HPA Predistorter

by Abdelhamid Louliej, Younes Jabrane, Víctor P. Gil Jiménez and Frédéric Guilloud

Electronics 2021, 10(13), 1538; https://doi.org/10.3390/electronics10131538 - 25 Jun 2021

Cited by 2 | Viewed by 1830

Abstract

Orthogonal Frequency Division Multiplexing (OFDM) is one of the key modulations for current and novel broadband communications standards. For example, Multi-band Orthogonal Frequency Division Multiplexing (MB-OFDM) is an excellent choice for the ECMA-368 Ultra Wideband (UWB) wireless communication standard. Nevertheless, the high Peak to Average Power Ratio (PAPR) of MB-OFDM UWB signals reduces the power efficiency of the key element in mobile devices, the High Power Amplifier (HPA), due to non-linear distortion, known as the non-linear saturation of the HPA. In order to deal with this limiting problem, a new and efficient pre-distorter scheme using a Neural Networks (NN) is proposed and also implemented on Field Programmable Gate Array (FPGA). This solution based on the pre-distortion concept of HPA non-linearities offers a good trade-off between complexity and performance. Some tests and validation have been conducted on the two types of HPA: Travelling Wave Tube Amplifiers (TWTA) and Solid State Power Amplifiers (SSPA). The results show that the proposed pre-distorter design presents low complexity and low error rate. Indeed, the implemented architecture uses 10% of DSP (Digital Signal Processing) blocks and 1% of LUTs (Look up Table) in case of SSPA, whereas it only uses 1% of LUTs in case of TWTA. In addition, it allows us to conclude that advanced machine learning techniques can be efficiently implemented in hardware with the adequate design. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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18 pages, 3976 KiB

Open AccessArticle

A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype

by Hamid Enayatollahi, Paul Sapin, Chinedu K. Unamba, Peter Fussey, Christos N. Markides and Bao Kha Nguyen

Electronics 2021, 10(13), 1535; https://doi.org/10.3390/electronics10131535 - 24 Jun 2021

Cited by 5 | Viewed by 1591

Abstract

This paper presents a control-oriented neuro-fuzzy model of brazed-plate evaporators for use in organic Rankine cycle (ORC) engines for waste heat recovery from exhaust-gas streams of diesel engines, amongst other applications. Careful modelling of the evaporator is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. The proposed adaptive neuro-fuzzy inference system (ANFIS) model consists of two separate neuro-fuzzy sub-models for predicting the evaporator output temperature and evaporating pressure. Experimental data are collected from a 1-kWe ORC prototype to train, and verify the accuracy of the ANFIS model, which benefits from the feed-forward output calculation and backpropagation capability of the neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using gradient-descent least-square estimate (GD-LSE) and particle swarm optimisation (PSO) techniques is investigated, and the performance of both techniques are compared in terms of RMSEs and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both. Training the ANFIS models using the PSO algorithm improved the obtained test data RMSE values by 29% for the evaporator outlet temperature and by 18% for the evaporator outlet pressure. The accuracy and speed of the model illustrate its potential for real-time control purposes. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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13 pages, 1256 KiB

Open AccessArticle

Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

by Javier Olarte, Jaione Martínez de Ilarduya, Ekaitz Zulueta, Raquel Ferret, Unai Fernández-Gámiz and Jose Manuel Lopez-Guede

Electronics 2021, 10(11), 1353; https://doi.org/10.3390/electronics10111353 - 6 Jun 2021

Cited by 5 | Viewed by 2217

Abstract

Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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17 pages, 4483 KiB

Open AccessArticle

Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems

by Dina Emara, Mohamed Ezzat, Almoataz Y. Abdelaziz, Karar Mahmoud, Matti Lehtonen and Mohamed M. F. Darwish

Electronics 2021, 10(11), 1261; https://doi.org/10.3390/electronics10111261 - 25 May 2021

Cited by 54 | Viewed by 4729

Abstract

Recently, the penetration of energy storage systems and photovoltaics has been significantly expanded worldwide. In this regard, this paper presents the enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load. DC–DC and DC–AC converters are coordinated and controlled to achieve DC voltage stability in the microgrid. To achieve such an ambitious target, the system is widely operated in two different modes: stand-alone and grid-connected modes. The novel control strategy enables maximum power generation from the photovoltaic system across different techniques for operating the microgrid. Six different cases are simulated and analyzed using the MATLAB/Simulink platform while varying irradiance levels and consequently varying photovoltaic generation. The proposed system achieves voltage and power stability at different load demands. It is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode. In general, the proposed battery converter control introduces a stable operation and regulated DC voltage but with few voltage spikes. The merit of the integrated DC microgrid with batteries is to attain further flexibility and reliability through balancing power demand and generation. The simulation results also show the system can operate properly in normal or abnormal cases, thanks to the proposed control strategy, which can regulate the voltage stability of the DC bus in the microgrid with energy storage systems and photovoltaics. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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21 pages, 8605 KiB

Open AccessArticle

Proposal of a Decoupled Structure of Fuzzy-PID Controllers Applied to the Position Control in a Planar CDPR

by Marco Carpio, Roque Saltaren, Julio Viola, Cristian Calderon and Juan Guerra

Electronics 2021, 10(6), 745; https://doi.org/10.3390/electronics10060745 - 22 Mar 2021

Cited by 10 | Viewed by 2465

Abstract

The design of robot systems controlled by cables can be relatively difficult when it is approached from the mathematical model of the mechanism, considering that its approach involves non-linearities associated with different components, such as cables and pulleys. In this work, a simple and practical decoupled control structure proposal that requires practically no mathematical analysis was developed for the position control of a planar cable-driven parallel robot (CDPR). This structure was implemented using non-linear fuzzy PID and classic PID controllers, allowing performance comparisons to be established. For the development of this research, first the structure of the control system was proposed, based on an analysis of the cables involved in the movement of the end-effector (EE) of the robot when they act independently for each axis. Then a tuning of rules was carried out for fuzzy PID controllers, and Ziegler–Nichols tuning was applied to classic PID controllers. Finally, simulations were performed in MATLAB with the Simulink and Simscape tools. The results obtained allowed us to observe the effectiveness of the proposed structure, with noticeably better performance obtained from the fuzzy PID controllers. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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14 pages, 4918 KiB

Open AccessArticle

Artificial Neural Network Controller for a Modular Robot Using a Software Defined Radio Communication System

by Luis Fernando Pedraza, Henry Alberto Hernández and Cesar Augusto Hernández

Electronics 2020, 9(10), 1626; https://doi.org/10.3390/electronics9101626 - 2 Oct 2020

Cited by 2 | Viewed by 2561

Abstract

Modular robots are flexible structures that offer versatility and configuration options for carrying out different types of movements; however, disconnection problems between the modules can lead to the loss of information, and, therefore, the proposed displacement objectives are not met. This work proposes the control of a chain-type modular robot using an artificial neural network (ANN) that enables the robot to go through different environments. The main contribution of this research is that it uses a software defined radio (SDR) system, where the Wi-Fi channel with the best signal-to-noise Ratio (SNR) is selected to send the information regarding the simulated movement parameters and obtained by the controller to the modular robot. This allows for faster communication with fewer errors. In case of a disconnection, these parameters are stored in the simulator, so they can be sent again, which increases the tolerance to communication failures. Additionally, the robot sends information about the average angular velocity, which is stored in the cloud. The errors in the ANN controller results, in terms of the traveled distance and time estimated by the simulator, are less than 6% of the real robot values. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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21 pages, 2639 KiB

Open AccessArticle

Prediction of Critical Flashover Voltage of High Voltage Insulators Leveraging Bootstrap Neural Network

by M. Tahir Khan Niazi, Arshad, Jawad Ahmad, Fehaid Alqahtani, Fatmah AB Baotham and Fadi Abu-Amara

Electronics 2020, 9(10), 1620; https://doi.org/10.3390/electronics9101620 - 2 Oct 2020

Cited by 7 | Viewed by 3354

Abstract

Understanding the flashover performance of the outdoor high voltage insulator has been in the interest of many researchers recently. Various studies have been performed to investigate the critical flashover voltage of outdoor high voltage insulators analytically and in the laboratory. However, laboratory experiments are expensive and time-consuming. On the other hand, mathematical models are based on certain assumptions which compromise on the accuracy of results. This paper presents an intelligent system based on Artificial Neural Networks (ANN) to predict the critical flashover voltage of High-Temperature Vulcanized (HTV) silicone rubber in polluted and humid conditions. Various types of learning algorithms are used, such as Gradient Descent (GD), Levenberg-Marquardt (LM), Conjugate Gradient (CG), Quasi-Newton (QN), Resilient Backpropagation (RBP), and Bayesian Regularization Backpropagation (BRBP) to train the ANN. The number of neurons in the hidden layers along with the learning rate was varied to understand the effect of these parameters on the performance of ANN. The proposed ANN was trained using experimental data obtained from extensive experimentation in the laboratory under controlled environmental conditions. The proposed model demonstrates promising results and can be used to monitor outdoor high voltage insulators. It was observed from obtained results that changing of the number of neurons, learning rates, and learning algorithms of ANN significantly change the performance of the proposed algorithm. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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Review

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29 pages, 6795 KiB

Open AccessReview

Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

by Wen Jiang, Yihui Ren, Ying Liu and Jiaxu Leng

Electronics 2022, 11(1), 156; https://doi.org/10.3390/electronics11010156 - 4 Jan 2022

Cited by 21 | Viewed by 8737

Abstract

Radar target detection (RTD) is a fundamental but important process of the radar system, which is designed to differentiate and measure targets from a complex background. Deep learning methods have gained great attention currently and have turned out to be feasible solutions in radar signal processing. Compared with the conventional RTD methods, deep learning-based methods can extract features automatically and yield more accurate results. Applying deep learning to RTD is considered as a novel concept. In this paper, we review the applications of deep learning in the field of RTD and summarize the possible limitations. This work is timely due to the increasing number of research works published in recent years. We hope that this survey will provide guidelines for future studies and applications of deep learning in RTD and related areas of radar signal processing. Full article

(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)

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