System Identification for Self-adaptive Control
Author: W. D. T. Davies
Publisher: John Wiley & Sons
Published: 1970
Total Pages: 404
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: W. D. T. Davies
Publisher: John Wiley & Sons
Published: 1970
Total Pages: 404
ISBN-13:
DOWNLOAD EBOOKAuthor: W.D.T. Davies
Publisher:
Published: 1970
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: W. D. T. Davies
Publisher:
Published: 1970
Total Pages: 380
ISBN-13:
DOWNLOAD EBOOKAuthor: Bahram Shafai
Publisher: Springer
Published: 2012-04-30
Total Pages: 500
ISBN-13: 9781461432029
DOWNLOAD EBOOKThis book offers comprehensive coverage of identification and adaptive control while familiarizing graduate students and practicing engineers with computational software tools such as MATLAB and SIMULINK and describing the underlying theoretical concepts. Identification is the process of mathematically modeling a system based on measurement data that may be limited or uncertain. Adaptive control is the means whereby a system that is poorly modeled is controlled adequately. Therefore the topical coverage is divided into two parts: Part I describes fundamental topics of system identification independent of adaptive control and discusses nonparametric and parameteric estimation methods while emphasizing least squares techniques instrumental variables and prediction error methods. Part II describes various methods of adaptive control in which the materials discussed in Part I are essential for control purposes, including model reference, adaptive control and self-tuning regulators.
Author: Yiannis Boutalis
Publisher: Springer Science & Business
Published: 2014-04-23
Total Pages: 316
ISBN-13: 3319063642
DOWNLOAD EBOOKPresenting 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 in: • contemporary power generation; • process control and • conventional benchmarking problems. Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results.
Author: P. H. Hammond
Publisher: Springer
Published: 2013-11-11
Total Pages: 360
ISBN-13: 1489962891
DOWNLOAD EBOOKAuthor: P. R. Kumar
Publisher: SIAM
Published: 2015-12-15
Total Pages: 371
ISBN-13: 1611974259
DOWNLOAD EBOOKSince its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
Author: P. R. Kumar
Publisher: SIAM
Published: 2015-12-15
Total Pages: 371
ISBN-13: 1611974267
DOWNLOAD EBOOKSince its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.?
Author: Dr. SHAIK RAFI KIRAN
Publisher: Lulu.com
Published:
Total Pages: 118
ISBN-13: 1329937201
DOWNLOAD EBOOKAuthor: Yiannis Boutalis
Publisher:
Published: 2014-05-31
Total Pages: 328
ISBN-13: 9783319063652
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