By Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao
The quantity LNCS 9377 constitutes the refereed court cases of the twelfth overseas Symposium on Neural Networks, ISNN 2015, held in jeju, South Korea on October 2015. The fifty five revised complete papers awarded have been rigorously reviewed and chosen from ninety seven submissions. those papers conceal many themes of neural network-related study together with clever regulate, neurodynamic research, memristive neurodynamics, desktop imaginative and prescient, sign processing, computer studying, and optimization.
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Extra resources for Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings
01 0 5 10 15 20 25 30 Time Fig. 2. The state response of system. 5 Conclusion In this paper, the problem of ﬁnite-time stabilization for Markov jump linear systems with partly known transition probabilities and time-varying polytopic uncertainties has been studied. By using the switched quadratic Lyapunov function, all the conditions are established in the form of LMIs. At last, the main result has been demonstrated through an illustrative example. 18 C. Zheng et al. References 1. : Analysis and synthesis of Markov jump linear systems with time-varying delays and partially known transition probabilities.
2. 2 Simplified Wind Turbine Model Fig. 2 is the simplified model of the doubly-fed induction generator (DFIG) . Vqr(t) and iqr(t) are the q-axis components of the rotor voltage and the rotor current. w(t) is the rotational speed, Tm(t) is the mechanical power, Ht is the equivalent inertia constant, Pe(t) is the active power. X2=1⁄Rr, X3=Lm ⁄Lss, T1=L0 ⁄(wsRs), L0=Lrr+Lm2 ⁄Lss, and Lss=Ls+Lm, Lrr=Lr+Lm, here Lm is the magnetizing inductance, Rr and Rs are the rotor and stator resistances, Lr and Ls are the rotor and stator leakage inductances, Lrr and Lss are the rotor and stator self-inductances, ws is the synchronous speed.
Uk (5) The optimal control law u∗ (xk ) can be expressed as u∗ (xk ) = arg min Q∗ (xk , uk ). uk (6) From (5), we know that if we obtain the optimal Q function Q∗ (xk , uk ), then the optimal control law u∗ (xk ) and the optimal performance index function J ∗ (xk ) can be obtained. However, the optimal Q function Q∗ (xk , uk ) is generally an unknown and non-analytic function, which cannot be obtained directly by (4). Hence, a discrete-time Q learning algorithm is developed in  to solve for the Q function iteratively.
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