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 on October 2015. The fifty five revised complete papers awarded have been conscientiously reviewed and chosen from ninety seven submissions. those papers disguise many issues of neural network-related learn together with clever keep an eye on, neurodynamic research, memristive neurodynamics, desktop imaginative and prescient, sign processing, computer 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 info for Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings
Liu and D. Qian Conclusions A terminal reaching law based sliding mode control method for the LFC problem is proposed in this article. The scheme is implemented in an interconnected power system with GRC and wind turbines. Moreover, RBF NNs are adopted to compensate and approximate the system uncertainties. The simulation results have validated the desirable frequency regulation performance against the system uncertainties, the GRC nonlinearity and wind power fluctuation. Compared with the SMC only, the superiority of the improved NNs-based sliding mode controllers has been illustrated.
Neural networks are employed to approximate the uncertainties, including the parametric variations and the unknown load-resistance. The actual control laws are derived by using the dynamic surface control method. Furthermore, a linear tracking differentiator is introduced to replace the ﬁrst-order ﬁlter to calculate the derivative of the virtual control law. Thus, the peaking phenomenon of the ﬁlter is suppressed during the initial phase. The system stability is analyzed by using the Lyapunov theory.
However, most of recent work above on the synchronization of chaotic CohenGrossberg networks has been restricted to the less general synchronization scheme as © Springer International Publishing Switzerland 2015 X. Hu et al. ): ISNN 2015, LNCS 9377, pp. 19–27, 2015. 1007/978-3-319-25393-0_3 20 M. Han and Y. Zhang complete synchronization, adaptive synchronization, etc. Function projective synchronization which is characterized by drive and response systems that can be synchronized up to a scaling function instead of a constant, is the extension of projective synchronization [12-13].
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