System Identification and Adaptive Control [electronic resource] : Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models / by Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou.Material type: TextLanguage: English Series: Advances in Industrial Control: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XII, 313 p. 120 illus., 56 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319063645Subject(s): Engineering | Artificial intelligence | Industrial engineering | Engineering | Control | Artificial Intelligence (incl. Robotics) | Computational Intelligence | Industrial and Production EngineeringAdditional physical formats: Printed edition:: No titleDDC classification: 629.8 LOC classification: TJ212-225Online resources: Click here to access online
Part I The Recurrent Neurofuzzy Model -- Introduction and Scope -- Identification of Dynamical Systems Using Recurrent Neurofuzzy Modeling -- Indirect Adaptive Control Based on the Recurrent Neurofuzzy Model -- Direct Adaptive Neurofuzzy Control of SISO Systems -- Direct Adaptive Neurofuzzy Control of MIMO Systems -- Selected Applications -- Part II The Fuzzy Cognitive Network Model: Introduction and Outline -- Existence and Uniqueness of Solutions in FCN -- Adaptive Estimation Algorithms of FCN Parameters -- Framework of Operation and Selected Applications.
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. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control. aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.