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 overseas convention on clever Computing, ICIC 2010, held in Changsha, China, in August 2010. The eighty five revised complete papers awarded have been conscientiously reviewed and chosen from a a variety of submissions. The papers are geared up in topical sections on neural networks, evolutionary studying & genetic algorithms, fuzzy concept and types, fuzzy structures and smooth computing, particle swarm optimization and area of interest know-how, 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, synthetic existence and synthetic immune structures, clever computing in snapshot processing, exact consultation on new hand established biometric equipment, targeted consultation on contemporary advances in snapshot segmentation, precise consultation on theories and purposes in complicated clever computing, detailed consultation on seek established software program engineering, particular consultation on bio-inspired computing and purposes, distinctive consultation on develop in dimensionality relief equipment and its functions, unique consultation on protein and gene bioinformatics: equipment and purposes

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Extra 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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Consider the map (3) endowed with the norm || ⋅ ||= ( ∑ || ⋅ || 2 1/ 2 ) L2 (4) . It is well known that if Eq. (3) is homeomorphism on R n , then (1) has a unique equilibrium point. First of all, we demonstrate the injective part. 20 X. Xu and J. , n : ∑ di (ui ) − di (vi ) = ∂ m k =1 ∂x k [ Dik ∂ (ui − vi ) ]+ ∂xk ∑ n j =1 ( aij + cij )[ g j (u j ) − g j (v j )] . , n ) and considering Assumption A1, Assumption A2, we get γ i (ui − vi ) 2 ≤ (ui − vi ) ∑ ∂ m k =1 ∂x k [ Dik ∂ (ui − vi ) ]+ | ui − vi | ∂xk ∑ n j =1 (| aij | + | cij |) L j | u j − v j | .

We can assume without loss of generality that neurons 1 through P are clamped and the remaining Q=L-P neurons are floating. At time m=0, the states of all the neurons form vector s (0) and the states of all the clamped neurons form vector s P (0) . The interconnect matrix T which is created by F is equal to FF + = ⎡T2 T1 ⎤ , where T4 ⎢T T ⎥ ⎣ 3 4⎦ is Q × Q matrix, then EAPNN is stable. What is its steady state value since EAPNN is stable? In the literature [3] a general expression and mathematical proof have been given.

J n )T . , n . e. e. , n . Let u ∗ = (u1∗ , u2∗ , h , u n∗ )T be the equilibrium point of (1). Global Exponential Robust Stability of HNN with Reaction-Diffusion Terms 19 Definition 1. The equilibrium point of (1) is said to be exponentially robustly stable if there exist constant M > 0 and λ > 0 such that for all J ∈ R n , A ∈ AI and C ∈ CI , || u (t , x ) − u ∗ ||≤ M || φ ( s , x ) − u ∗ || e − λ t ∗ || φ ( s, x) − u ||= sup s∈( −∞, 0 ] ∑ n || φi ( s, x ) − ui* || L2 i =1 holds, where t≥0 , x∈Ω , .

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