By Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao
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 conceal many issues of neural network-related study together with clever keep an eye on, neurodynamic research, memristive neurodynamics, computing device imaginative and prescient, sign processing, desktop studying, and optimization.
Read or Download Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings PDF
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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
Nonlinear Analysis: Real World Applications 14, 1182–1190 (2013) 14. : Complex function projective synchronization of complex chaotic system and its applications in secure communication. Nonlinear Dynamics 76, 1087–1097 (2014) 15. : Function projective synchronization of impulsive neural networks with mixed time-varying delays. Nonlinear Dynamics 78, 2627–2638 (2014) 16. : Generalized function projective lag synchronization between two different neural networks. , Zeng, Z. ) ISNN 2013, Part I. LNCS, vol.
It is clear that the parameters bˆij , cˆij adapt themselves to the true values bij , cij respectively. The numerical simulations clearly verify the effectiveness and feasibility of the proposed hybrid control method. Hybrid Function Projective Synchronization of Unknown Cohen-Grossberg 25 x2 (t ) x1 (t ) Fig. 1. 2 t Fig. 2. Time evolution of synchronization errors 5 t Fig. 3. Variation of unknown parameters Conclusions In this paper, we have dealt with the hybrid function projective synchronization problem for unknown Cohen-Grossberg neural networks with time delays and noise perturbation.
Adaptive backstepping voltage controller design for an PWM AC-DC converter. International Journal of Electrical and Power Engineering 1(1), 62–69 (2007) 8. : Design of backstepping power control for grid-side converter of voltage source converter-based high-voltage dc wind power generation system. IET Renewable Power Generation 7(2), 118–133 (2013) 9. : Dynamic surface control of nonlinear systems. In: Proceedings of the 1997 American Control Conference, Albuquerque, pp. 3028–3034 (1997) 10. : Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form.