Download Advances in Neural Networks – ISNN 2007: 4th International by Hongwei Wang, Hong Gu (auth.), Derong Liu, Shumin Fei, PDF

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

This booklet is a part of a 3 quantity set that constitutes the refereed complaints 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 offered 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 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 development acceptance, 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.

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Additional 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

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3. Adaptive parameters aˆ , bˆ, cˆ of Genesio system Fig. 4. 2. 3 and 4, respectively. 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.

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. Circuits, Systems and Signal Processing 24 (2005) 599-613 10. : Global Synchronization of Impulsive Coupled Delayed Neural Networks. Wang, J and Yi, Z. ): Advances in Neural Networks ISNN 2006.

Cost of Synchronizing Different Chaos Systems. Mathematics and Computers in Simulation 58 (2002) 309-327 3. : Lag Synchronization in Time-Delayed Systems. Physics Letter A 292 (2002) 320-324 4. : Periodic Response to External Stimulation of a Chaotic Neural Network with Delayed Feedback. International Journal of Bifurcation and Chaos 9 (1999) 713-722 5. : Neural Network Control for Nonlinear Chaotic Motion. Acta Physica Sinica 51 (2002) 2463-2466 6. : Adaptive Control for a Class of Chaotic Systems with Uncertain Parameters.

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