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

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https://doi.org/10.1057/9781137555694 Fuzzy Connection Admission Controller (NFCAC) was proposed in this paper. NFCAC has been successfully applied in the control-related problems of neural networks. However, there is no discussion about the power control game algorithm and location recognition based on NFCAC in cellular Ad Hoc network

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Policies for Higher Education Institutions,ain dryers. In this scheme, there are two RFNNs and two PI controllers. One RFNN with feedforeward and feedback connections of grain layer history position states predicts outlet moisture content (MPRFNN), and the other predicts the discharge rate of the dryer (RPRFNN). One PI controller adjusts the

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John S. Levin,Virginia Montero-Hernandez observers to estimate time derivatives of the system outputs. The weighting parameters of the fuzzy-neural controller are tuned on-line via a genetic algorithm (GA). For the purpose of on-line tuning the weighting parameters of the fuzzy-neural controller, a Lyapunov-based fitness function of the G

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Juliet Lilledahl Scherer,Mirra Leigh Ansonthe system. In this paper, neural network based multiple model adaptive predictive control method is proposed to solve this problem. The whole control system is composed of predictive controller and decision controller. First of all, neural network model set of any possible environment is built up,

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Juliet Lilledahl Scherer,Mirra Leigh Ansons the construction of its skeletal and muscular components, a dynamic model for flapping wing is developed. As the model is highly nonlinear and coupled with unmeasurable disturbances and uncertainties, traditional strategies are not applicable for flapping wing motion control. A new approach called

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Lecture Notes in Computer Scienceand bounded perturbations. The uncertainty is assumed to be unknown continuous function without norm-bounded restriction. The perturbation is sector-bounded. Combined with liner matrix inequality method, neural networks and adaptive control, the control scheme ensures the stability of the close-loop

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Takao Terano,Setsuya Kurahashi,Ushio Minami Considering the nonlinearity of the system, when the set-point value is time variant, a controller based on a fixed structure RBFNN can not give a good control performance. A switching controller which switches among different controller based on different RBFNN is used to adapt the varing set-poin