System Identification and Adaptive Control: Theory and by Yiannis Boutalis, Visit Amazon's Dimitrios Theodoridis Page,

By Yiannis Boutalis, Visit Amazon's Dimitrios Theodoridis Page, search results, Learn about Author Central, Dimitrios Theodoridis, , Visit Amazon's Theodore Kottas Page, search results, Learn about Author Central, Theodore Kottas, , Manolis A. Christodoulou

Presenting present tendencies within the improvement and functions of clever platforms in engineering, this monograph specializes in contemporary study leads to procedure id and keep an eye on. The recurrent neurofuzzy and the bushy cognitive community (FCN) versions are provided. either versions are appropriate for partially-known or unknown advanced time-varying structures. Neurofuzzy Adaptive keep an eye on includes rigorous proofs of its statements which bring about concrete conclusions for the choice of the layout parameters of the algorithms offered. The neurofuzzy version combines suggestions from fuzzy structures and recurrent high-order neural networks to supply robust process approximations which are used for adaptive keep watch over. The FCN version stems from fuzzy cognitive maps and makes use of the thought of “concepts” and their causal relationships to trap the habit of complicated structures. The booklet indicates how, with the good thing about right education algorithms, those versions are effective method emulators compatible to be used in engineering platforms. All chapters are supported by way of illustrative simulation experiments, whereas separate chapters are dedicated to the aptitude business functions of every version together with initiatives in:

• modern strength generation;

• method keep watch over and

• traditional benchmarking problems.

Researchers and graduate scholars operating in adaptive estimation and clever regulate will locate Neurofuzzy Adaptive regulate of curiosity either for the foreign money of its types and since it demonstrates their relevance for actual structures. The monograph additionally indicates commercial engineers find out how to try clever adaptive keep an eye on simply utilizing confirmed theoretical results.

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Extra resources for System Identification and Adaptive Control: Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models

Example text

SIAM, Advances in Design and Control Series. 20 1 Introduction and Scope of Part I Ioannou, P. , & Sun, J. (1996). Robust adaptive control. Englewood Cliffs, NJ: Prentice-Hall. Ioannou, P. A. & Tsakalis, K. S. (1986). A robust direct adaptive controller. IEEE Transactions on Automatic Control, AC-31(11), 10331043. Jang, J. S. R. (1993). Anfis: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23, 665–684. Jou, I. , Chang, C. , & Chen, H. K. (1999).

A more analytical and general description of the novel method of parameter hopping is presented in Chap. 3 Sect. 2. The above procedure is depicted in Fig. 3, in a simplified two-dimensional representation. The magnitude of hopping is − κl Pil x¯ fi W lf (x¯ fi )T i tr{(x¯ fi )T x¯ fi } being determined by following the vectorial proof given in Chap. 3 (where b = W lfi and our plane is described by equation x¯ fi · W lfi = ρl , with x¯ fi the normal to it), with κl a positive constant (such as, 0 < κl Pil < 1) decided appropriately from the designer and Pil is the l-th element of the gain matrix Pi .

2012). Dynamical recurrent neuro-fuzzy identification schemes employing switching parameter hopping. International Journal of Neural Systems, 22, 16. Theodoridis, D. , Christodoulou, M. , & Boutalis, Y. S. (2008). Indirect adaptive neuro— fuzzy control based on high order neural network function approximators. Proccedings of the 16th Mediterranean Conference on Control and Automation—MED08 (pp. 386–393). Corsica, France: Ajaccio. Theodoridis, D. , Boutalis, Y. S. & Christodoulou, M. A. (2009b).

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