Advances in Neural Networks – ISNN 2013: 10th International by Qinglai Wei, Derong Liu (auth.), Chengan Guo, Zeng-Guang

By Qinglai Wei, Derong Liu (auth.), Chengan Guo, Zeng-Guang Hou, Zhigang Zeng (eds.)

The two-volume set LNCS 7951 and 7952 constitutes the refereed court cases of the tenth overseas Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised complete papers provided have been conscientiously reviewed and chosen from a number of submissions. The papers are prepared in following themes: computational neuroscience, cognitive technological know-how, neural community types, studying algorithms, balance and convergence research, kernel tools, huge margin equipment and SVM, optimization algorithms, varational equipment, keep an eye on, robotics, bioinformatics and biomedical engineering, brain-like structures and brain-computer interfaces, info mining and information discovery and different functions of neural networks.

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Extra info for Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II

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Chaos theory is epitomized by the so-called ‘butterfly effect’ detailed by Lorenz [10]. Until now, chaotic behavior has already been observed in the laboratory in a variety of systems including electrical circuits, lasers, oscillating chemical reactions, fluid dynamics, as well as computer models of chaotic processes. Chaos theory has been applied to a number of fields, among which one of the most applications was in ecology, where dynamical systems have been used to show how population growth under density dependence can lead to chaotic dynamics.

The variation of the chaotic variable has a delicate inherent rule in spite of the fact that its variation looks like in disorder. Therefore, after each search round, we can conduct the chaotic search in the neighborhood of the current optimal parameters by listing a certain number of new generated parameters through chaotic process. In this way, we can make use of the ergodicity and irregularity of the chaotic variable to help the algorithm to jump out of the local optimum as well as finding the optimal parameters.

Control situations. , representation of nonstationary state variables by using a finite number of different stationary patterns, and recognition techniques for identification and classification of stationary patterns, are not suitable to cope with these problems. A new framework is required to implement pattern-based learning, recognition and control in a unified way. Recently, a deterministic learning (DL) theory was proposed for identification, recognition and control of nonlinear dynamical systems undergoing periodic or recurrent motions [5].

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