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Titlebook: Advances in Neural Networks - ISNN 2017; 14th International S Fengyu Cong,Andrew Leung,Qinglai Wei Conference proceedings 2017 Springer Int

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楼主: 哥哥大傻瓜
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Stephen White,John Gardner,Tony Saicholler in simulation. Using the designed control architecture, the sufficient robust stable conditions are derived in the presence of uncertainties of modelling and measurement errors, and the control output tracking performance is also realized.
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National Socialism and Social Capital,. The Lyapunov-krasovskii functionals (LKFs) are utilized to eliminate and compensate the effect of unknown functions with time-varying delayed states in communication channels. By using the Lyapunov analysis, the stability of closed-loop systems is proven.
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0302-9743 2017, held in Sapporo, Hakodate, and Muroran, Hokkaido, Japan, in June 2017..The 135 revised full papers presented in this two-volume set were carefully reviewed and selected from 259 submissions. The papers cover topics like perception, emotion and development, action and motor control, attractor
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https://doi.org/10.1007/978-1-349-14514-0e precise total demanded power is unavailable and there involve modeling uncertainties as well as external disturbances. To address the issue of unknown power trajectory, an analytical model is proposed to reconstruct the unknown desired power profile. Neural network based control scheme is developed to ensure stable power tracking.
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Communists in the Face of Fascists,neural networks to approximate control vectors and performance index function. Finally, this paper focuses on an example simulated on Matlab, which verifies the excellent convergence of the mentioned algorithm and feasibility of this scheme.
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https://doi.org/10.1007/978-1-349-14514-0is compensated by NN based identification. To illustrate the validity of the proposed controller, numerical simulation is performed and the comparison between the NN based controller and a conventional proportional-derivative (PD) controller is conducted.
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