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Titlebook: System Identification and Adaptive Control; Theory and Applicati Yiannis Boutalis,Dimitrios Theodoridis,Manolis A. Book 2014 Springer Inte

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書目名稱System Identification and Adaptive Control
副標題Theory and Applicati
編輯Yiannis Boutalis,Dimitrios Theodoridis,Manolis A.
視頻videohttp://file.papertrans.cn/885/884469/884469.mp4
概述Summarizes the latest studies in neurofuzzy control.Explains how to apply two powerful models in a variety of systems.Provides the reader with mutually reinforcing rigorous theoretical proof and simul
叢書名稱Advances in Industrial Control
圖書封面Titlebook: System Identification and Adaptive Control; Theory and Applicati Yiannis Boutalis,Dimitrios Theodoridis,Manolis A. Book 2014 Springer Inte
描述.Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented.?Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model?stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems.?All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects
出版日期Book 2014
關鍵詞Adaptive Control; Adaptive Estimation; Fuzzy Cognitive Maps; Fuzzy Cognitive Networks; Neurofuzzy Models
版次1
doihttps://doi.org/10.1007/978-3-319-06364-5
isbn_softcover978-3-319-35412-5
isbn_ebook978-3-319-06364-5Series ISSN 1430-9491 Series E-ISSN 2193-1577
issn_series 1430-9491
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Direct Adaptive Neurofuzzy Control of SISO Systemss zero very fast. In case the modeling error term depends also on a not necessarily known constant value, then it is proved that the error remains bounded. Next, an appropriate state feedback is constructed to achieve asymptotic regulation of the output, while keeping bounded all signals in the clos
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Selected Applicationsd to any control task requiring a reliable system approximator, provided that the assumptions made in the analysis is met. A detailed treatment of the design of robust adaptive controller for the robotic manipulator with uncertainties in the field of applications is provided. The controller is appro
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Book 2014e benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems.?All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects
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s to rule out most of the nonsense possibilities, combined with storing the remaining possibilities as uncertainty in the database and resolving these during querying by means of user feedback, seems . promising solution. In this chapter we introduce this “good is good-enough” integration approach a
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s to rule out most of the nonsense possibilities, combined with storing the remaining possibilities as uncertainty in the database and resolving these during querying by means of user feedback, seems . promising solution. In this chapter we introduce this “good is good-enough” integration approach a
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