Abstract:Hydraulic interconnected suspension is a nonlinear system, whose modeling method by mechanism can’t both have modeling accuracy and speed. In order to solve the contradiction, a modeling method for HIS system based on genetic algorithm optimized BP neural network was proposed. Firstly, the training data was obtained by the simulation of hydraulic interconnected suspension in Simulink. Secondly, genetic algorithm was used to optimize the initial weights and thresholds of the BP neural network. Thirdly, The two modeling methods were compared to verify the advantages of GABP modeling method. Finally, the experimental data obtained by the experiment of bench and the results simulated by the neural network were compared and analyzed. The results show that the relative error percentages of the low, medium, and high are 4.12%, 2.27%, and 1.51% in the vertical mode respectively; The relative error percentages of the low, medium, and high are 7.64%, 4.07% and 4.35% in the roll made respectively. It is concluded that Compared with the mechanism modeling method, the GABP modeling method has better modeling accuracy and speed at the same time.