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A novel adaptive radial basis function neural network H-infinity control strategy with robust feedback compensator using linear matrix inequality (LMI) approach is proposed for micro electro mechanical systems vibratory gyroscopes involving parametric uncertainties and external disturbances. The proposed system is comprised of a neural network controller, which is designed to mimic an equivalent control...
A new robust neural sliding mode(RNSM) tracking control scheme using radial basis function(RBF) neural network (NN) is presented for MEMS (MicroElectroMechanical systems) Z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed...
According to the diversity and nonlinear characteristics of effect factors for COD concentration, a model of predicting COD concentration for surface water environment monitoring section was established based on the BP Neural Network technology. The model was applied to predict COD concentration for Xiaojiemiao monitoring section of Taizi river in Anshan area in this paper. The results showed that...
In this paper, a joint power and subcarrier allocation scheme in single-cell uplink OFDMA systems is proposed based on game theory. Our goal is to maximize the power efficiency of each user under peak power constraint. For the purpose, a utility function is designed which can enhance the rate per joule of energy consumed greatly, and the existence and uniqueness of the Nash equilibrium of the function...
To solve the problems in modulation recognition of time-frequency overlapped two-signal in the single-channel, an algorithm based on cyclic cumulants used modulation recognition is presented. Through computing the fourth-order cyclic cumulants of pretreated band signals, the feature vector for classification was extracted, and then use the minimum-error criterion to classify time-frequency overlapped...
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