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 offered have been conscientiously reviewed and chosen from a various submissions. The papers are geared up in topical sections on neural networks, evolutionary studying & genetic algorithms, fuzzy conception and types, fuzzy platforms and tender 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 knowledge mining, synthetic existence and synthetic immune structures, clever computing in photo processing, distinct consultation on new hand dependent biometric tools, detailed consultation on contemporary advances in photo segmentation, detailed consultation on theories and functions in complicated clever computing, specific consultation on seek dependent software program engineering, unique consultation on bio-inspired computing and purposes, specified consultation on develop in dimensionality relief tools and its purposes, targeted consultation on protein and gene bioinformatics: tools and functions
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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
IEEE Trans. CAS 36, 846–857 (1989) 2. : Target signal detection and target localization. Naval University of Engineering, Wuhan (2001) 3. : Sufficient and necessary condition of the extended alternating projection neural network configured as a content addressable memory. Tien Tzu Hsueh Pao/Acta Electronica Sinica 32, 596–600 (2004) 4. : An extrapolation algorithm for band-limited signals based on Alternating Projection Neural Networks. Acta Electronica Sinica 28, 52–55 (2000) 5. : Notch filter based on Complex Alternating Projection Neural Networks.
Then is the interconnect matrix obtained by the practical method possible to ensure that APNN is stable? If possible, what is stable convergent condition of network used for CAM? In addition, all the researches on APNN have been done in the real domain. In order to APNN can be widely applied to signal processing, its application scope needs to be expanded from the real domain to the complex domain. Is it possible to directly expand? If impossible, how do we modify the network? Aiming at the above problems, the literature  studies APNN thoroughly and proposes a new neural network—Extended Alternating Projection Neural Network (EAPNN) which functions in the complex domain.
N . 5 ∑ 0. 5 n j =1 ∑ n j =1 L j (| aij | + | cij |)]ξ i χ 0 + L j [| aij |+ | cij | κ ij (λ )]ξ j χ 0 < 0 . This is a contradiction. 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 .