By Huang D.-S., Zhao Z., Bevilacqua V., Figueroa J.C. (eds.)

This publication constitutes the refereed court cases of the sixth foreign convention on clever Computing, ICIC 2010, held in Changsha, China, in August 2010. The eighty five revised complete papers offered have been rigorously reviewed and chosen from a quite a few submissions. The papers are equipped in topical sections on neural networks, evolutionary studying & genetic algorithms, fuzzy thought and types, fuzzy platforms and gentle computing, particle swarm optimization and area of interest expertise, supervised & semi-supervised studying, unsupervised & reinforcement studying, combinatorial & numerical optimization, structures biology and computational biology, neural computing and optimization, nature encouraged computing and optimization, wisdom discovery and information mining, man made lifestyles and synthetic immune structures, clever computing in picture processing, specified consultation on new hand established biometric equipment, exact consultation on contemporary advances in picture segmentation, certain consultation on theories and functions in complex clever computing, distinctive consultation on seek dependent software program engineering, certain consultation on bio-inspired computing and purposes, distinct consultation on develop in dimensionality relief tools and its functions, precise consultation on protein and gene bioinformatics: equipment and functions

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Additional resources for Advanced intelligent computing theories and applications : 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings

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Cui Theorem 2. The first P elements of inital state vector s (0) of EAPNN form vector sP (0) , The remaining Q elements form vector s Q (0) , the first P rows of F form the matrix FP , the remaining Q rows form the matrix FQ , A = R ( F ) is a subspace enclosed by all library patterns, B is a set of all vectors whose first P elements form s P (0) , then s (∞ ) in the subspace B can be written as follows: ⎡ ⎤ sP (0) sB (∞) = ⎢ ⎥ + + + Q + P + P ⎢⎣FQ (I − FP FP )(FQ (I − FP FP )) (s (0) − FQFP s (0)) + FQFP s (0)⎥⎦ (2) s (∞ ) in the subspace A is equal to ⎡ ⎤ FPFP+sP (0) sA(∞) = ⎢ ⎥ + + + Q + P + P F ( I − F F )( F ( I − F F )) ( s ( 0 ) − F F s ( 0 )) + F F s ( 0 ) P P Q P P Q P Q P ⎣⎢ Q ⎦⎥ (3) 3 Several Conclusions of EAPNN Theorem 2 obtains a general expression which includes all kinds of situations.

The chosen basis function type for all these time-variant algorithms is Prolate [6] and the number of bases have been set to 3. Varying the basis function type and number leads to different performances, but similar comparative conclusions as those drawn in following subsections can be done: therefore, for the sake of conciseness, such results have not been reported here. 14 Y. Ye, S. Squartini, and F. 5, (b = 1, 2, 3). The inputs are normalized (within the range [−1, 1]). In the simulation of single perceptron system, hidden neurons are gradually increase one by one from 1 to 20.

Huang et al. ): ICIC 2010, LNCS 6215, pp. 25–32, 2010. © Springer-Verlag Berlin Heidelberg 2010 26 Y. Ge, S. Ma, and X. Luo controlling nonlinear systems with flexible dynamics[3]. Consequently, the direct inverse model control is selected as the control system of CSTR in this paper. Neural network is one of the most applied methods in identification and control of the nonlinear systems. Along with the development of artificial neural networks, they are applied for inverse control of nonlinear systems in many works[4-7].

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