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 booklet constitutes the refereed complaints 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 conscientiously reviewed and chosen from a a number of submissions. The papers are equipped in topical sections on neural networks, evolutionary studying & genetic algorithms, fuzzy conception and versions, fuzzy structures and smooth computing, particle swarm optimization and area of interest expertise, 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 information mining, man made lifestyles and synthetic immune structures, clever computing in picture processing, unique consultation on new hand dependent biometric equipment, specific consultation on contemporary advances in snapshot segmentation, unique consultation on theories and functions in complicated clever computing, designated consultation on seek established software program engineering, detailed consultation on bio-inspired computing and functions, unique consultation on boost in dimensionality relief tools and its functions, distinctive consultation on protein and gene bioinformatics: tools 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

Example text

This conclusion can be theoretically proved. Theorem 3. Suppose that EAPNN have L neurons (L is changeless), and have learned a group of library patterns. Assuming that the first P neurons are clamped and the remaining Q = L − P neurons are floating. Let T4 denote the interconnect matrix formed by weights of all floating neurons, then the spectral radius, ρ (T 4 ) , of T4 will decrease with increase of P . In order to prove the theorem, a definition and a theorem—Courant maximin theorem need to be introduced.

E. 5 . From Definition 1, the zero solution of (12) is globally exponentially robustly stable, namely, the equilibrium point of (1) is globally exponentially robustly stable. The proof is completed. , n , x ∈ Ω , (18) where J i = 0 , qij (s) = e− s , i , j = 1, 2. , n , x ∈ Ω . 5,1] ⎤ , so A0 = ⎡ 1 2 ⎤ , C 0 = ⎡2 1 ⎤ . 5, 1]⎥⎦ d1 (u1 ) = 6u1 , d 2 (u 2 ) = 5u 2 , g1 (u1 ) = tanh u1 , g 2 (u2 ) = tanh u 2 . Apparently, γ 1 = 6 , γ 2 = 5 , ⎡sin x2 2 + cos 2 x1 ⎤ L1 = L2 = 1 . Taking D( x ) = ⎢ ⎥ , | xk |≤ 3 (1 ≤ k ≤ 2) .

From the above corollary we can learn that s A (∞) = s BQ (∞ ) is unrelated to Q sQ (0) while rank ( FP ) = rank ( F ) , that is to say, final steady states of the network are unrelated to the inital states of the floating neurons. This shows that the steady convergent condition of the network used for CAM is to require rank( FP ) = rank( F ) . Apparently, when all the rows of F are linearly independent, the steady convergent condition of EAPNN agrees with that of APNN. G Corollary 2. If rank ( F P ) = rank ( F ) and s P ( 0 ) = F P ⋅ k , where G G T Q Q k = [k 1 k 2 " k N ] is constant vector, then s A (∞) = s B (∞) = FQ ⋅ k .

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