Read e-book online Advances in Neural Networks – ISNN 2013: 10th International PDF

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

ISBN-10: 3642390676

ISBN-13: 9783642390678

ISBN-10: 3642390684

ISBN-13: 9783642390685

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 offered have been rigorously reviewed and chosen from a variety of submissions. The papers are equipped in following subject matters: computational neuroscience, cognitive technological know-how, neural community types, studying algorithms, balance and convergence research, kernel equipment, huge margin equipment and SVM, optimization algorithms, varational equipment, keep watch over, robotics, bioinformatics and biomedical engineering, brain-like structures and brain-computer interfaces, information mining and information discovery and different purposes of neural networks.

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Read Online or Download Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II PDF

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

Sample text

Firstly, different control situations are defined as dynamical patterns and are identified via deterministic learning (DL). When the dynamical pattern is correctly classified, the corresponding NN learning controller with knowledge or experience is selected. Secondly, by adopting a class of switching signals with average dwell time (ADT) property , it is shown that the NN learning controller can achieve small tracking errors and fast convergence rate with small control gains. These results will guarantee not only stability of the closed-loop systems, but also better performance in the aspects of time saving or energy saving.

Repeat the selection and updating operation until reaching the stopping criteria. 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.

Note that Theorem 1 implies, if a unknown pattern is recognized to be similar with one of the patterns of systems (1) and resemble another pattern 32 F. Yang and C. Wang as a result of environment change later, the closed-loop systems remain stable when satisfying the ADT property. In practical systems, reference models sometimes may also happen to change for some reasons, which produce several other cases, such as same system tracks different recurrent reference models, which is similar to Theorem 1, hence, remark below describes another more complicated case.

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Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II by Qinglai Wei, Derong Liu (auth.), Chengan Guo, Zeng-Guang Hou, Zhigang Zeng (eds.)


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