Sequential Intelligent Dynamic System Modeling and Control [electronic resource] / by Hai-Jun Rong, Zhao-Xu Yang.

Por: Rong, Hai-Jun [author.]Colaborador(es): Yang, Zhao-Xu [author.] | SpringerLink (Online service)Tipo de material: TextoTextoEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XI, 259 p. 69 illus., 12 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819715411Tema(s): Control engineering | Dynamics | Nonlinear theories | Computational intelligence | Control and Systems Theory | Applied Dynamical Systems | Computational IntelligenceFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 629.8312 | 003 Clasificación LoC:TJ212-225Recursos en línea: Libro electrónicoTexto
Contenidos:
1 Fuzzy Inference Systems -- 2 Neural Networks -- 3 Optimization Algorithms -- 4 Modeling and Control of Nonlinear Dynamic Systems -- 5 Online Sequential Fuzzy Extreme Learning Machine -- 6 Sequential Adaptive Fuzzy Inference System -- 7 Evolving Fuzzy Systems based on Data Clouds -- 8 Stability of A Class of Evolving Fuzzy Systems -- 9 Adaptive Self-Learning Fuzzy Autopilot Design for Uncertain Bank-to-Turn Missiles -- 10 Self-Evolving Fuzzy Model-based Controller for Hypersonic Vehicle -- 11 Self-Evolving Data Cloud-based PID-like Controller for Nonlinear Uncertain Systems -- 12 Adaptive Nonparametric Evolving Fuzzy Controller for Nonlinear Uncertain Systems with Dead Zone -- 13 Adaptive Backstepping Neural Controller for Magnetic Bearing System -- 14 Simplified Adaptive Backstepping Neural Controller for Magnetic Bearing System -- 15 Robust Kernel-based Model Reference Adaptive Control for Unstable Aircraft.
En: Springer Nature eBookResumen: The book offers novel research results of sequential intelligent dynamic system modeling and control in a unified framework from theory proposals to real applications. It covers an in-depth study of various learning algorithms for the permanent adaptation of intelligent model parameters as well as of structural parts of the model. The comprehensive researches on sequential fuzzy and neural controller design schemes for some complex real applications are included. This is particularly suited for readers who are interested to learn practical solutions for controlling nonlinear systems that are uncertain and varied at any time. In addition, the organization of the book from addressing fundamental concepts, and presenting novel intelligent models to solving real applications is one of the major features of the book, which makes it a valuable resource for both beginners and researchers wanting to further their understanding and study about realtime online intelligent modeling and control of nonlinear dynamic systems. The book can benefit researchers, engineers, and graduate students in the fields of control engineering, artificial intelligence, computational intelligence, intelligent control, nonlinear system modeling, and control, etc.
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1 Fuzzy Inference Systems -- 2 Neural Networks -- 3 Optimization Algorithms -- 4 Modeling and Control of Nonlinear Dynamic Systems -- 5 Online Sequential Fuzzy Extreme Learning Machine -- 6 Sequential Adaptive Fuzzy Inference System -- 7 Evolving Fuzzy Systems based on Data Clouds -- 8 Stability of A Class of Evolving Fuzzy Systems -- 9 Adaptive Self-Learning Fuzzy Autopilot Design for Uncertain Bank-to-Turn Missiles -- 10 Self-Evolving Fuzzy Model-based Controller for Hypersonic Vehicle -- 11 Self-Evolving Data Cloud-based PID-like Controller for Nonlinear Uncertain Systems -- 12 Adaptive Nonparametric Evolving Fuzzy Controller for Nonlinear Uncertain Systems with Dead Zone -- 13 Adaptive Backstepping Neural Controller for Magnetic Bearing System -- 14 Simplified Adaptive Backstepping Neural Controller for Magnetic Bearing System -- 15 Robust Kernel-based Model Reference Adaptive Control for Unstable Aircraft.

The book offers novel research results of sequential intelligent dynamic system modeling and control in a unified framework from theory proposals to real applications. It covers an in-depth study of various learning algorithms for the permanent adaptation of intelligent model parameters as well as of structural parts of the model. The comprehensive researches on sequential fuzzy and neural controller design schemes for some complex real applications are included. This is particularly suited for readers who are interested to learn practical solutions for controlling nonlinear systems that are uncertain and varied at any time. In addition, the organization of the book from addressing fundamental concepts, and presenting novel intelligent models to solving real applications is one of the major features of the book, which makes it a valuable resource for both beginners and researchers wanting to further their understanding and study about realtime online intelligent modeling and control of nonlinear dynamic systems. The book can benefit researchers, engineers, and graduate students in the fields of control engineering, artificial intelligence, computational intelligence, intelligent control, nonlinear system modeling, and control, etc.

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