By Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao

ISBN-10: 3319253921

ISBN-13: 9783319253923

ISBN-10: 331925393X

ISBN-13: 9783319253930

The quantity LNCS 9377 constitutes the refereed lawsuits of the twelfth overseas Symposium on Neural Networks, ISNN 2015, held in jeju, South Korea on October 2015. The fifty five revised complete papers offered have been conscientiously reviewed and chosen from ninety seven submissions. those papers disguise many issues of neural network-related learn together with clever regulate, neurodynamic research, memristive neurodynamics, laptop imaginative and prescient, sign processing, computer studying, and optimization.

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Additional resources for Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings

Sample text

First, the procedure of the Qlearning based policy iteration ADP algorithm is described. Next, property analysis of the Q-learning based policy iteration ADP algorithm is established. It is proven that the iterative Q functions will monotonically non-increasing and converges to the optimal solution of the HJB equation. Finally, simulation results will illustrate the effectiveness of the developed algorithm. The rest of this paper is organized as follows. In Section 2, the problem formulation is presented.

For the applications in [3, 4], only conventional power sources have been taken into account. Nowadays, wind power is a power generation way with most promise. However, the randomness and the intermittency of wind plant pose a critical challenge to real-time stability and balancing of power systems [5]. Therefore, the increasing penetration of wind generation in power systems calls for more and more attentions to the LFC problem for the power systems with nonconventional generation systems [6].

Finally, simulation results are presented to show the performance of the developed algorithm. Keywords: Adaptive critic designs, adaptive dynamic programming, approximate dynamic programming, Q-learning, policy iteration, neural networks, nonlinear systems, optimal control. 1 Introduction Characterized by strong abilities of self-learning and adaptivity, adaptive dynamic programming (ADP), proposed by Werbos [25, 26], has demonstrated powerful capability to find the optimal control policy by solving the Hamilton-Jacobi-Bellman (HJB) equation forward-in-time and becomes an important brain-like intelligent optimal control method for nonlinear systems [4, 6–9, 12, 17, 23].

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Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings by Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao


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