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Sequential intelligent dynamic syste...

Rong, Hai-Jun.

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Sequential intelligent dynamic system modeling and control

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sequential intelligent dynamic system modeling and control/ by Hai-Jun Rong, Zhao-Xu Yang.
作者:	Rong, Hai-Jun.
其他作者:	Yang, Zhao-Xu.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xi, 259 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Nonlinear systems. -
電子資源:	https://doi.org/10.1007/978-981-97-1541-1
ISBN:	9789819715411

Sequential intelligent dynamic system modeling and control
Rong, Hai-Jun.

Sequential intelligent dynamic system modeling and control[electronic resource] /by Hai-Jun Rong, Zhao-Xu Yang. - Singapore :Springer Nature Singapore :2024. - xi, 259 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9789819715411

Standard No.: 10.1007/978-981-97-1541-1doiSubjects--Topical Terms:

686475
Nonlinear systems.

LC Class. No.: QA402

Dewey Class. No.: 003

Sequential intelligent dynamic system modeling and control
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505 0 $a 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.
520 $a 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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