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 traits within the improvement and purposes of clever platforms in engineering, this monograph specializes in fresh study ends up in procedure identity and regulate. The recurrent neurofuzzy and the bushy cognitive community (FCN) types are awarded. either versions are appropriate for partially-known or unknown advanced time-varying structures. Neurofuzzy Adaptive regulate comprises 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 platforms and recurrent high-order neural networks to provide robust method approximations which are used for adaptive keep an eye on. The FCN version stems from fuzzy cognitive maps and makes use of the inspiration of “concepts” and their causal relationships to catch the habit of advanced platforms. The publication indicates how, with the advantage of right education algorithms, those versions are effective procedure emulators appropriate to be used in engineering structures. All chapters are supported through illustrative simulation experiments, whereas separate chapters are dedicated to the capability business purposes of every version together with tasks in:
• modern energy generation;
• strategy keep watch over and
• traditional benchmarking problems.
Researchers and graduate scholars operating in adaptive estimation and clever regulate will locate Neurofuzzy Adaptive keep an eye on of curiosity either for the forex of its types and since it demonstrates their relevance for actual platforms. The monograph additionally exhibits commercial engineers the way to try clever adaptive regulate simply utilizing confirmed theoretical results.
Read or Download System Identification and Adaptive Control: Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models PDF
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Extra resources for System Identification and Adaptive Control: Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models
Sample text
1992). Fuzzy basis functions, universal approximation, and orthogonal least squares learning. IEEE Transactions on Neural Networks, 3, 807–814. , & Dai, S. L. (2007). Direct adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems. Fuzzy Sets and Systems, 158, 2655–2670. , & Hsu, C. (2001). Robust adaptive fuzzy-neural control of nonlinear dynamical systems using generalized projection update law and variable structure controller. IEEE Transactions on SMC-Part B, 31, 140–147.
39) implies that V ∈ L ∞ , and therefore W˜ fi ∈ L ∞ . Then, Eq. 36) in conjunction with the boundedness of z i , gives ei ∈ L ∞ . Furthermore, W fi = W˜ fi + W ∗fi is also bounded. Since V is a nonincreasing function of time and bounded from below, the limt∈∞ V = V∞ exists; therefore, by integrating V˙ from 0 to ∞ we have 1 2 ∞ n ei 2 ≤ [V (0) − V∞ ] < ∞, 0 i=1 which implies that ei ∈ L 2 . 30) we have W˙ fi ≤ |ei | |z i | ⊥Pi ⊥ . 40) Since x¯ fi , Pi , z i , ei ∈ L ∞ , and ei ∈ L 2 , we have W˙ fi ∈ L 2 ∩ L ∞ .
M. (1997). Adaptive fuzzy control: experiments and comparative analyses. IEEE Transactions on Fuzzy Systems, 5, 167–188. , & Yu, T. (1989). Robustness of adaptive controllers: a survey. Automatica, 25, 651–678. Passino, K. & Yurkovich, S. (1998). Fuzzy control. Menlo Park: Addison Wesley Longman. Polycarpou, M. M. (1996). Stable adaptive neural control scheme for nonlinear systems. IEEE Transactions on Automatic Control, 41, 447–450. Poznyak, A. , Sanchez, E. , & Perez, J. P. (1999). Nonlinear adaptive trajectory tracking using dynamic neural networks.
