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 complaints 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 rigorously reviewed and chosen from a variety of submissions. The papers are geared up in following issues: 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, regulate, robotics, bioinformatics and biomedical engineering, brain-like platforms and brain-computer interfaces, information mining and information discovery and different purposes of neural networks.

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

**Example text**

We can also prove that every subsystem of the control systems (1) is locally exponentially stable in the same way. , Lemma 2, because all the systems in the family (1) are proved to be locally exponentially stable, then for each p ∈ K there exists a Lyapunov function Vp (x) that for some positive constants αp , βp and γp satisﬁes αp (|x|) ≤ Vp (x) ≤ βp (|x|), and ∇Vp (x)fp (x) < −γp (|x|), ∀x ∈ Rn , p ∈ K. Undoubtedly, we obtain ∇Vp (x)fp (x) ≤ −λp Vp (x), p ∈ K, where λp = γp /βp . This implies that Vp (x(t0 + τ )) ≤ exp(−λp τ )Vp (x(t0 )), p ∈ K.

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.

When t ∈ [tk , tk+1 ), we say that σ(tk )th subsystem is activated and the trajectory x(t) of system (1) is the trajectory of the σ(tk )th subsystem. fσ (x) are the unknown nonlinearities, corresponding to diﬀerent operating environments such as a normal state and changes in system dynamics, faults in the system, sensor failures, and external disturbances. Firstly, we design the subsystem state x(t) in all the environments to track a set of periodic or periodic-like reference orbits xd (t) generated from the following reference models x˙ d1 = xd2 , x˙ d2 = fd (xd ), (2) x˙ d1 = xd2 , x˙ d2 = fdm (xd ), (3) or where xd = [xd1 , xd2 ]T is the system state, fd (·) or fdm (·)(m = 1, · · · , M ) is the smooth nonlinear function and (2) is the case when the reference orbit remains unchanged but system dynamics fσ (x) changes due to some reasons, whereas (3) is another case when there are diﬀerent reference tracking orbits xm d corresponding to changes in initial conditions or system parameters.

### 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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