Fuzzy Modeling and Control: Methods, Applications and Research

Terrell Harvey, Dallas Mullins (Editors)

Series: Mathematics Research Developments
BISAC: MAT029000

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Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick

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Fuzzy Modeling and Control: Methods, Applications and Research opens by recommending a new fuzzy RANSAC algorithm based on the reinforcement learning concept to improve modeling performance under the outlier noise. The authors also propose a novel methodology for online modeling of multivariable Hammerstein evolving fuzzy models with minimum realization in state space from experimental data. Results characterized by strongly coupled nonlinearities demonstrate the computational efficiency of the proposed methodology. Later, two types of neural networks are applied to find the approximate solutions of the fully fuzzy nonlinear system, and a superior gradient descent algorithm is proposed in order to train the neural networks. Lastly, the authors propose a novel online evolving fuzzy Takagi-Sugeno state-space model identification approach for nonlinear multivariable systems. To circumvent “the curse of dimensionality”, the algorithm uses tools for monitoring the quality of the existing clusters.

Preface

Chapter 1. A Fuzzy RANSAC Algorithm Based on the Reinforcement Learning Concept for Modeling
(Toshihiko Watanabe, Faculty of Engineering, Osaka Electro-Communication University, Neyagawa, Osaka, Japan)

Chapter 2. Multivariable Fuzzy Hammerstein Model Identification from Evolving Data Clustering
(Jéssica A. Santos and Ginalber L. O. Serra, Department of Electrotechnical and Electronic, Federal University of Maranhão, São Luís, Brazil, and others)

Chapter 3. Neural Network Approach to Solving Fully Fuzzy Nonlinear Systems
(Sina Razvarz, Raheleh Jafari Alexander Gegov, Wen Yu and Satyam Paul, Departamento de Control Autom´atico, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico, and others)

Chapter 4. An Evolving Method Applied to the Multivariable Fuzzy Modeling from Experimental Data
(Luís M. M. Torres and Ginalber L. O. Serra, Federal Institute of Maranhão, Imperatriz, Brazil, and others)

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