A kind of new dynamic modeling method based on improved genetic wavelet neural networks for the robot wrist force sensor

This paper presents a method used to the robot wrist force sensor modeling based on improved genetic wavelet neural networks (IGWNN) and the principle of algorithm is introduced. In this method, the dynamic model of the wrist force sensor is set up according to data of the dynamic calibration, where the structure and parameters of wavelet neural networks of the dynamic model are optimized by genetic algorithm. The results show that the proposed method can overcome the shortcomings of easy convergence to the local minimum points of BP algorithm, and the network complexity, the convergence and the generalization ability are well compromised and the training speed and precision of model are increased.