Application of Neural Network Trained by Adaptive Particle Swarm Optimization to Fault Diagnosis for Steer-by-Wire System

A new particle swarm optimization algorithm with dynamically changing inertia weight and threshold value based on improved adaptive particle swarm optimization is proposed, in which the inertia weight of the particle is adjusted adaptively based on the premature convergence degree of the swarm and the fitness of the particle. The diversity of inertia weight makes a compromise between the global convergence and local convergence speed, so it can effectively alleviate the problem of premature convergence. The algorithm is applied to train neural network and a model of fault diagnosis for steer-by-wire is established, compared with particle swarm optimization algorithm and genetic algorithm, the proposed algorithm can effectively improve the training efficiency of neural network and obtain good diagnosis results.