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

The 262 revised lengthy papers and 192 revised brief papers provided have been rigorously reviewed and chosen from a complete of 1,975 submissions. The papers are geared up in topical sections on neural fuzzy keep watch over, neural networks for keep an eye on purposes, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear structures 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 functions, feedforward neural networks, recurrent neural networks, neural networks for optimization, aid vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and purposes of neural networks.

**Read 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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This publication is a part of a 3 quantity set that constitutes the refereed complaints of the 4th overseas Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. The 262 revised lengthy papers and 192 revised brief papers provided have been conscientiously reviewed and chosen from a complete of 1,975 submissions.

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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**

Let χ denote a region of interest in the phase space that contains the chaotic attractor of system (1). The synchronization schemes (1) and (2) are said to be uniformly quasi-synchronized with error bound ε > 0 if there exists a T ≥ t0 such that ||x(t) − y(t)|| ≤ ε for all t ≥ T starting from any initial values x(t0 ) ∈ χ and y(t0 ) ∈ χ. Deﬁnition 2. ([23]) A function V : R+ × Rn → R+ is said to belong to class Σ if 1) V is continuous in (τk−1 , τk ) × Rn and, for each x ∈ Rn , k = 1, 2, · · · , lim(t,y)→(τ +,x) V (t, y) = V (τk+ , x) exists; k 2) V is locally Lipschitzian in x For the following general impulsive diﬀerential equation x˙ = g(t, x), t = τk , x(τk+ ) = ψk (x(τk )), t = τk , x(t0 ) = x0 , t0 ≥ 0, (5) the right-upper Dini’s derivative of V ∈ Σ is deﬁned as the following: Deﬁnition 3.

11) i=1 Calculating the upper Dini derivative of V (t) with respect to time along the solution N vi (t) = 0, we can get for t = tk , of Eq. (10), from Condition (A1 ), and note that i=1 N n D+ V (t) ≤ − δr − cr + (a0rr )+ kr + i=1 r=1 + 1 2 1 2 n (|a0rs |ks + |a0sr |kr ) s=1 s=r n 2 |aτrs |ls vir (t) + s=1 1 2 n N 2 |aτsr |lr vir (t − τ ) + s=1 vi (t) i=1 N × bij Γ vj (t) + diag(δ1 , . . , δn )vi (t) j=1 n v¯j (t)(γj B + δj IN )¯ vj (t), ≤ −pV (t) + qV (t − τ ) + (12) j=1 where v¯j (t) = (¯ v1j (t), · · · , v¯N j (t)) ∈ L def = z = (z1 , · · · , zN ) ∈ RN | n 0 , from which it can be concluded that if γj λ(γj )+δj ≤ 0, then N i=1 zi = v¯j (t)(γj B+ j=1 δj IN )¯ vj (t) ≤ 0.

Physica A 366 (2006) 197-211 6. : Robust Impulsive Synchronization of Coupled Delayed Neural Networks with Uncertainties. Physica A 373 (2006) 261-272 7. : Adaptive Synchronization of Coupled Chaotic Systems Based on Parameters Identiﬁcation and Its Applications. Int. J. Bifur. Chaos 16 (2004) 2923-2933 8. : Robust Synchronization of Delayed Neural Networks Based on Adaptive Control and Parameters Identiﬁcation. Chaos, Solitons, Fractals 27 (2006) 905-913 9. : Chaotic Lag Synchronization of Coupled Delayed Neural Networks and Its Applications in Secure Communication.

### 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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