Titlebook: Advances in Neural Networks - ISNN 2007; 4th International Sy Derong Liu,Shumin Fei,Changyin Sun Conference proceedings 2007 Springer-Verla

发起

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Elena Zheleva,Evimaria Terzi,Lise Getoor the control law are derived using the Lyapunov method so that the entire system stability and the network convergence are guaranteed. The developed method is evaluated by computer simulation using the well-known mean value engine model (MVEM) and the effectiveness of the method is proved.

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https://doi.org/10.1057/9781137555694f each node can all be optimized due to the minimum link occupation with the help of the algorithm. Simulation results show that the novel algorithm can give more power control guarantee to cellular Ad Hoc networks in the variable node loads and transmitting powers, and make the node more stable to support multi-hops at the same time.

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John S. Levin,Virginia Montero-Hernandezme guarantees that all signals involved are bounded and the outputs of the closed-loop system track the desired output trajectories. Finally, simulation results are provided to demonstrate robustness and applicability of the proposed method.

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Juliet Lilledahl Scherer,Mirra Leigh Ansonwitch among all predictive controllers according to performance target. This method can ensure stability and performance of the system. Finally, simulation results show effectiveness of the proposed method.

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Juliet Lilledahl Scherer,Mirra Leigh Ansontem behavior to improve its performance during system operation. Furthermore, much less information about the system dynamics is needed in construction such a control scheme as compared with traditional NN based methods. Both theoretical analysis and computer simulation verify its effectiveness.

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Michael J. Zakour,David F. Gillespie. These advantages are verified by its application to a practical temperature controlled box, which is used in medicinal inspection. The proposed system presents better behavior than that when using traditional back-propagation neural network.

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https://doi.org/10.1007/978-1-4614-5737-4controller and configuration of an adaptive neural network controller. Then give some simulation figures to illustrate defect for the new controller. Finally we will develop a hybrid neural network to solve the problem and improve the accuracy as well as reduce the cost to the least in the practical application.

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