Aiming at the complexity of data processing in Weigh-In-Motion (WIM) system, a nonlinear system model is built for the WIM system with radical basic function (RBF) neural network. To achieve more accurate network weights of RBF and improve the model detection precision, a novel chaos immune algorithm is presented to optimize the RBF network weights. In this paper, the logistic equation is used to generate the initial population and the chaos disturbance is used to improve the searching efficiency of immune algorithm. Experiment results show that this nonlinear model is effective, and it can reduce the detection error for nonlinearity and time-varying of WIM system and improve the detection precision. Compared to the simple RBF network model, the proposed RBF network optimized by chaos immune algorithm owns high measuring precision.
['Yi Shen', 'Yunfeng Bu', 'Mingxin Yuan']
Study on Weigh-in-Motion System Based on Chaos Immune Algorithm and RBF Network