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 lawsuits of the tenth foreign Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised complete papers provided have been rigorously reviewed and chosen from various submissions. The papers are prepared in following themes: computational neuroscience, cognitive technological know-how, neural community versions, studying algorithms, balance and convergence research, kernel equipment, huge margin tools and SVM, optimization algorithms, varational tools, regulate, robotics, bioinformatics and biomedical engineering, brain-like platforms and brain-computer interfaces, info mining and data 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
4. Comparative results of step response curves by using EDA and CEDA 5 Conclusions In this paper, an improved estimation of distribution algorithm-CEDA is proposed. The chaos theory is introduced into the basic EDA, and a better performance can be attained in this way. Comparative experimental results of the proposed CEDA andbasic EDA are also given to verify the feasibility and effectiveness of our proposed approach, which provide a more effective way for the optimization of flight control parameters.
2456–2461 (2011) 11. : An iterative -optimal control scheme for a class of discrete-time nonlinear systems with unfixed initial state. Neural Networks 32, 236–244 (2012) 12. : Model-free multiobjective approximate dynamic programming for discrete-time nonlinear systems with general performance index functions. Neurocomputing 72(7-9), 1839–1848 (2009) 13. : Advanced forecasting methods for global crisis warning and models of intelligence. General Systems Yearbook 22, 25–38 (1977) 14. : A menu of designs for reinforcement learning over time.
The main contributions of this paper are given as follows. First, By adopting a class of switching signals with ADT property, which means that the number of switches in a ﬁnite interval is bounded and the average time between consecutive switching is not less than a constant , it is shown that the NN learning controller can achieve better performance. Second, the synchronously switched stabilization problem of NN control systems with ADT is studied by designing a set of pattern-based NN controllers and by ﬁnding a set of switching signals with admissible ADT.