By Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao
The quantity LNCS 9377 constitutes the refereed court cases of the twelfth foreign Symposium on Neural Networks, ISNN 2015, held in Jeju, South Korea in October 2015. The fifty five revised complete papers provided have been conscientiously reviewed and chosen from ninety seven submissions. These papers conceal many subject matters of neural network-related study together with clever keep watch over, neurodynamic research, memristive neurodynamics, desktop imaginative and prescient, sign processing, computing device studying, and optimization.
Read Online or Download Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15-18, 2015, Proceedings (Lecture Notes in Computer Science) PDF
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Additional info for Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15-18, 2015, Proceedings (Lecture Notes in Computer Science)
Wei and D. Liu the traditional iterative ADP algorithms. Q functions depend on both system state and control, which means that they already include the information about the system and the utility function. Hence, it is easier to compute control policies from Q functions than the traditional performance index functions. Because of this merit, Q-learning algorithms are preferred to unknown and model-free systems to obtain the optimal control. In this paper, inspired by , a novel Q-learning based policy iteration ADP algorithm is developed for discrete-time nonlinear systems.
The hybrid controller and parameter update law are designed as follows: U = U1 + U 2 + U 3 + U 4 , U1 = − Ke(t ) K = diag ( k1 , k2 ,… , kn ) ki = λi ei 2 (t ) U 2 = D( M (t ) x(t )) Λ( M (t ) x(t )) − M (t ) D( x(t ))Λ( x(t )) + D( M (t ) x(t )) J − M (t ) D( x(t )) J (5) U 3 = M (t ) D( x(t )) Bf ( x(t )) − D( y (t )) Bf ( M (t ) x(t )) + M (t ) x(t ) U 4 = M (t ) D( x(t ))Cg ( x(t − τ )) − D( y (t ))Cg ( M (t − τ ) x(t − τ )) Δbij = − wij ei (t )di ( yi (t )) f j ( y j (t )), Δcij = −qij ei (t )di ( yi (t )) g j ( y j (t − τ )) (6) where U1 is an adaptive controller and U 2 ,U3 ,U 4 are open-loop controllers.
218, 5769–5781 (2012) 7. : Delay-dependent exponential stability for Markovian jumping stochastic Cohen-Grossberg neural networks with p-Laplace diﬀusion and partially known transition rates via a diﬀerential inequality. Ad. Diﬀer. Equa. 2013, 1–14 (2013) 8. : Finite-time control of linear systems subject to parametric uncertainties and disturbances. Auto. 37, 1459–1463 (2001) 9. : Finite-time stabilization by state feedback control for a class of time-varying nonlinear systems. Auto. 48, 499–504 (2012) 10.