Advanced intelligent computing theories and applications : by Huang D.-S., Zhao Z., Bevilacqua V., Figueroa J.C. (eds.)

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 various submissions. The papers are prepared in topical sections on neural networks, evolutionary studying & genetic algorithms, fuzzy idea and types, fuzzy platforms and smooth computing, particle swarm optimization and area of interest know-how, supervised & semi-supervised studying, unsupervised & reinforcement studying, combinatorial & numerical optimization, platforms biology and computational biology, neural computing and optimization, nature encouraged computing and optimization, wisdom discovery and knowledge mining, man made lifestyles and synthetic immune structures, clever computing in snapshot processing, exact consultation on new hand established biometric tools, designated consultation on contemporary advances in photograph segmentation, exact consultation on theories and functions in complex clever computing, precise consultation on seek dependent software program engineering, particular consultation on bio-inspired computing and purposes, exact consultation on increase in dimensionality aid tools and its functions, detailed consultation on protein and gene bioinformatics: tools and functions

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What is its steady state value since EAPNN is stable? In the literature [3] a general expression and mathematical proof have been given. It can be denoted by the following theorem 2. 36 J. Wang, Y. Wang, and X. 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.

Extreme learning machine: Theory and applications. Neurocomputing 70(1-3), 489–501 (2006) 3. : Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 17(4), 879 (2006) 4. : Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16-18), 3460–3468 (2008) 5. : Error minimized extreme learning machine with growth of hidden nodes and incremental learning 20(8), 1352–1357 (2009) 6. : A new time-variant neural based approach for nonstationary and non-linear system identification.

2 Time-Variant MLP System Identification The system to be identified here is the same with that in [6] and [7]: a timevariant IIR-buffered MLP with 11 input lines, one 5 neurons hidden layer, one neuron output layer. The input weights and output weights are combination of 3 Chebyshev basis functions; the lengths of the input and output TDLs are equal to 6 and 5 respectively. Note that the output neuron of this system is not linear, both the hidden neurons and output neuron use tangent sigmoid activation function.

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