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001 978-981-97-1541-1
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020 _a9789819715411
_9978-981-97-1541-1
024 7 _a10.1007/978-981-97-1541-1
_2doi
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aGPFC
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTJFM
_2thema
100 1 _aRong, Hai-Jun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980118
245 1 0 _aSequential Intelligent Dynamic System Modeling and Control
_h[electronic resource] /
_cby Hai-Jun Rong, Zhao-Xu Yang.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXI, 259 p. 69 illus., 12 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1 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 _aThe 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.
650 0 _aControl engineering.
650 0 _aDynamics.
_965524
650 0 _aNonlinear theories.
_965525
650 0 _aComputational intelligence.
_964052
650 1 4 _aControl and Systems Theory.
_965868
650 2 4 _aApplied Dynamical Systems.
_965530
650 2 4 _aComputational Intelligence.
_964052
700 1 _aYang, Zhao-Xu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980119
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819715404
776 0 8 _iPrinted edition:
_z9789819715428
776 0 8 _iPrinted edition:
_z9789819715435
856 4 0 _uhttps://doi.org/10.1007/978-981-97-1541-1
912 _aZDB-2-INR
912 _aZDB-2-SXIT
999 _c61385
_d61385