Model Identification of Magneto-Rheological Mount Based on Genetic Algorithms and BP Neural Network

Initial weights and thresholds of BP neural network are optimized by using Genetic Algorithm(GA) method to solve its slow convergence speed and local optimum. The defect of BP neural network is thus overcome by the proposed method. The direct and inverse dynamic models for a prototype of Magneto-rheological (MR) mount are identified by using traditional BP neural network and novel GA-BP neural network. The results show that the GA-BP neural network has faster convergence rate and higher precision compared with the traditional BP neural network in the identification of direct and inverse model for MR mount.