System Identification and Adaptive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook System Identification and Adaptive Control write by Yiannis Boutalis. This book was released on 2014-04-23. System Identification and Adaptive Control available in PDF, EPUB and Kindle. 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 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.
System Identification for Self-adaptive Control
System Identification for Self-adaptive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook System Identification for Self-adaptive Control write by W. D. T. Davies. This book was released on 1970. System Identification for Self-adaptive Control available in PDF, EPUB and Kindle.
Stochastic Systems
Stochastic Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Systems write by P. R. Kumar. This book was released on 2015-12-15. Stochastic Systems available in PDF, EPUB and Kindle. Since 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.
System Identification and Adaptive Control
System Identification and Adaptive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook System Identification and Adaptive Control write by Bahram Shafai. This book was released on 2012-04-30. System Identification and Adaptive Control available in PDF, EPUB and Kindle. This 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.
Identification and Stochastic Adaptive Control
Identification and Stochastic Adaptive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Identification and Stochastic Adaptive Control write by Han-fu Chen. This book was released on 2012-12-06. Identification and Stochastic Adaptive Control available in PDF, EPUB and Kindle. Identifying the input-output relationship of a system or discovering the evolutionary law of a signal on the basis of observation data, and applying the constructed mathematical model to predicting, controlling or extracting other useful information constitute a problem that has been drawing a lot of attention from engineering and gaining more and more importance in econo metrics, biology, environmental science and other related areas. Over the last 30-odd years, research on this problem has rapidly developed in various areas under different terms, such as time series analysis, signal processing and system identification. Since the randomness almost always exists in real systems and in observation data, and since the random process is sometimes used to model the uncertainty in systems, it is reasonable to consider the object as a stochastic system. In some applications identification can be carried out off line, but in other cases this is impossible, for example, when the structure or the parameter of the system depends on the sample, or when the system is time-varying. In these cases we have to identify the system on line and to adjust the control in accordance with the model which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners.
