Download Advanced Intelligent Computing Theories and Applications. by De-Shuang Huang, Laurent Heutte, Marco Loog PDF

By De-Shuang Huang, Laurent Heutte, Marco Loog

This ebook - at the side of the 2 volumes LNCS 4681 and LNAI 4682 - constitutes the refereed complaints of the 3rd foreign convention on clever Computing, ICIC 2007, held in Qingdao, China in August 2007. The clever computing expertise features a variety of suggestions reminiscent of synthetic intelligence, perceptual and trend reputation, evolutionary and adaptive computing, informatics theories and functions, computational neuroscience and bioscience, delicate computing, case established and limited reasoning, brokers, networking and laptop supported co-operative operating, human computing device interface matters. ICIC subject unifies the image of up to date clever computing concepts as an essential idea that highlights the traits in complex computational intelligence and bridges theoretical study with functions.

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Wang, and W. Sun At the initial learning phase, there are no neurons in the hidden layer, and the first input observation data is viewed as the first neuron. As for the following input, we can judge whether to add neurons or not according to the above two criteria. 2 Merging Neurons In order to simplify the network structure and decrease redundant rules, these highly similar hidden neurons should be merged after each parameter learning operation is finished. We put forward the following relationship to measure the similarity between two hidden neurons.

ICIC 2007, CCIS 2, pp. 29–38, 2007. © Springer-Verlag Berlin Heidelberg 2007 30 F. Salehi, R. Azizi, and A. Salehi decided by running the reservoir simulator by placing the wells at various locations in the reservoir. In field development studies, a large number of scenarios which result in a time consuming and expensive process must be considered. Artificial neural network are predictive tools capable of solving highly non-linear problems. A neural net consists of a large number of simple processing elements called neurons.

Remark 1. Because ||T ||∞ = max{|A∗ |∞ , |B ∗ |∞ }, ||T ||∞ < 1 is equivalent to |A∗ |∞ < 1 and |B ∗ |∞ < 1. Denote T1 = 0 B A , we have the following corollary. 0 Corollary 1. For System (1), if ||T1 ||∞ < 1, then z ∗ is locally delay-dependent asymptotical stability so long as z ∗ is an equilibrium of System (1). Proof. Because 0 ≤ φ(u) ≤ 1, u ∈ IR, n m ||T ||∞ = max = max ≤ max |a∗ik |}, max { 1≤i≤n k=1 m i=1 max n+1≤k≤n+m |bki φ(x∗i )|} { i=1 n |aik |}, k=1 |b∗ki |} { n k=1 m max { 1≤i≤n n+1≤k≤n+m |aik φ(yk∗ )|}, max { 1≤i≤n max max n+1≤k≤n+m |bki |} { i=1 = ||T1 ||∞ < 1 By using Theorem 1, Corollary 1 holds.

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