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 conscientiously reviewed and chosen from quite a few 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, keep an eye on, robotics, bioinformatics and biomedical engineering, brain-like platforms and brain-computer interfaces, information mining and information discovery and different purposes of neural networks.
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Extra resources for Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II
Omitted here due to the limited space. 5 Conclusions This paper considered the distributed tracking control of uncertain nonlinear multi-agent systems with unmeasured states and unknown input of leader. Distributed observer-based tracking controllers with static and dynamic coupling gains are developed, based on the observed states of neighboring agents. It is proved with the developed controllers, synchronization to the leader can be reached for any undirected connected graphs, and all signals in the closed-loop network are uniformly ultimately bounded.
Duan Estimation of distribution algorithm is novel kind of optimization algorithm. It is a combination of genetic algo orithms and statistical learning . Nowadays it has becoome a significant method dealin ng with programming problems such as the optimizationn of flight control system. Besid des, estimation of distribution algorithms was put forwardd as a significant issue in almost every academic seminar such as ACMEVO, IEEE and CEC. Nevertheless, it can easily trap into the local optimum, hence would probaably end up without finding a saatisfying result.
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.