Read e-book online Advances in Neural Networks – ISNN 2007: 4th International PDF

By Hongwei Wang, Hong Gu (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)

ISBN-10: 3540723927

ISBN-13: 9783540723929

ISBN-10: 3540723935

ISBN-13: 9783540723936

This booklet is a part of a 3 quantity set that constitutes the refereed court cases of the 4th foreign Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007.

The 262 revised lengthy papers and 192 revised brief papers awarded have been conscientiously reviewed and chosen from a complete of 1,975 submissions. The papers are geared up in topical sections on neural fuzzy keep an eye on, neural networks for keep watch over purposes, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear platforms modeling, robotics, balance research of neural networks, studying and approximation, facts mining and have extraction, chaos and synchronization, neural fuzzy structures, education and studying algorithms for neural networks, neural community buildings, neural networks for trend reputation, SOMs, ICA/PCA, biomedical purposes, feedforward neural networks, recurrent neural networks, neural networks for optimization, aid vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and functions of neural networks.

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Read Online or Download Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II PDF

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Extra resources for Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II

Sample text

Obviously, the synchronization errors converge asymptotically to zero and two different systems are indeed achieved chaos synchronization. Furthermore, the estimates of parameters converge to their real values. 5 Conclusions This paper presents two chaos synchronization schemes between unified chaotic system and Genesio system with different structures and parameters. Active control is used when system parameters are known and adaptive control is used when system parameters are unknown. Computer simulations show the effectiveness of the proposed schemes.

8) Assume that, in addition to (A1 ), the following conditions are satisfied for all i = 1, 2, · · · , n and k ∈ Z + = {1, 2, · · · , ∞} (A2 ) There exist n positive numbers δ1 , · · · , δn , and two numbers pi = δi + ci − (a0ii )+ ki − 1 2 n |a0ij |kj + |a0ji |ki j=1 − 1 2 n |aτij |lj , j=1 j=i qi = 1 2 n |aτji |li , j=1 such that p = min1≤i≤n {2pi } > q = ⎧max1≤i≤n {2qi } and γi λ(γi ) + δi ≤ 0, where ⎨ λ2 , if γi > 0, (a0ii )+ = max{a0ii , 0} with λ(γi ) = 0, if γi = 0, ⎩ λN , if γi < 0. 20 L.

Then k 0 0 s→0 k u(t) ≤ ( θk )e−μ(t−t0 ) ( sup t0 −τ ≤s≤t0 t0 0 is a solution of the inequality μ−p+qeμτ ≤ 0. 3 Robust Impulsive Synchronization Base on impulsive control theory on delayed dynamical systems, the following sufficient condition for robust impulsive synchronization of the controlled coupled delayed neural network (2) is established. Theorem 1. Consider the controlled coupled delayed neural network (2). Let the impulsive controller as +∞ ui (t, xi ) = +∞ dk xi (t− k ) − s(t) δ(t − tk ), Iik (t, xi (t))δ(t − tk ) = k=1 (7) k=1 where dk is a constant called as the control gain, δ(t) is the Dirac function, and the eigenvalues of its coupling matrix B be ordered as 0 = λ1 > λ2 ≥ λ3 ≥ · · · , λN .

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Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II by Hongwei Wang, Hong Gu (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)


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