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In this paper, two different types of neural networks are investigated and employed for the online solution of strictly-convex quadratic minimization; i.e., a two-layer back-propagation neural network (BPNN) and a discrete-time Hopfield-type neural network (HNN). As simplified models, their error-functions could be defined directly as the quadratic objective function, from which we further derive...
To accomplish the demand of continuously variable transmission (CVT) fault diagnosis, the structure of CVT fault diagnosis system is built and the application model of Back-Propagation Neural Network is established aiming at the features of CVT faults. The structure and 3 algorithms of network are devised. The network proposed is simulated and the results are analyzed in detail. The simulation results...
Based on the nonlinearity in direct torque control (DTC) system, a modified PSO (particle swarm optimization) algorithm is proposed to optimize BP (back-propagation) neural network and structure the rotational speed identifier. Combined a linear digression method of inertia weight with a particle turning laws, this algorithm can accelerate the convergence speed of BP neural network and realize global...
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