By Hongwei Wang, Hong Gu (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)
This ebook 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 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 regulate purposes, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear platforms modeling, robotics, balance research of neural networks, studying and approximation, information mining and have extraction, chaos and synchronization, neural fuzzy platforms, education and studying algorithms for neural networks, neural community buildings, neural networks for development acceptance, SOMs, ICA/PCA, biomedical functions, feedforward neural networks, recurrent neural networks, neural networks for optimization, help vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and functions of neural networks.
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Extra info for Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II
Global Robust Stability of Delayed Neural Networks. IEEE Trans. Circ. Syst. I, 50(2003)156-160 2. : Eﬀect of Parameter Mismatch on The Mechanism of Chaos Synchronization Loss in Coupled Systems. Physical Review E 58 (1998) 5620-5628 3. : Global Synchronization in Arrays of Delayed Neural Networks with Constant and Delayed Coupling. PHysics Letters A, 353(2006) 318-325 4. : Synchronization Criteria of Lur’e Systems with Time-delay Feedback Control. Chaos Solitons & Fractals 23 (2005) 1285-1298 5.
It may be an equilibrium point, a periodic orbit, or a chaotic attractor. Throughout this paper, we deﬁne the synchronization state of the N 1 controlled coupled delayed neural network (2) as s(t) = xi (t), where N i=1 xi (t) (i = 1, 2 · · · , N ) are the solutions of the continuous coupled delayed neural network (1) . For the later use, the deﬁnition with respect to robust impulsive synchronization of the controlled coupled delayed neural network (2) and the famous Halanay diﬀerential inequality on impulsive delay diﬀerential inequality are introduced as follows: Deﬁnition 1.
H. Guan, and T. Li ⎧ e1 = (25α + 10)( y1 − x1 ) − y + u1 ⎪ ⎨ e2 = (28 − 35α ) x1 + (29α − 1) y1 − x1 z1 − z + u2 ⎪⎩ e3 = x1 y1 − 8 +α z1 − ax − by − cz − x 2 + u3 3 (9) where e1 = x1 − x, e2 = y1 − y, e3 = z1 − z Our goal is to find proper controllers ui (i = 1, 2,3) and parameter adaptive laws, such that system (8) globally synchronizes system (2) asymptotically. e. lim e(t ) = 0 t →∞ where e = [e1 , e2 , e3 ]T Theorem: If the controllers are chosen as ⎧ u1 = −(25αˆ + 10)( y1 − x1 ) + y − k1e1 ⎪ ⎨ u2 = −(28 − 35αˆ ) x1 − (29αˆ − 1) y1 + x1 z1 + z − k2 e2 ⎪ u = − x y + 8 +αˆ z + ax ˆ + cz ˆ + by ˆ + x 2 − k3 e3 1 1 1 ⎩ 3 3 (10) And adaptive laws of parameters are chosen as ⎧ aˆ = − xe3 ⎪ ⎪bˆ = − ye3 ⎨ ⎪cˆ = − ze3 ⎪αˆ = 25( y − x )e − (35 x − 29 y )e − 1 z e 1 1 1 1 1 2 ⎩ 3 1 3 (11) Then system (8) globally synchronizes system (2) asymptotically.