关键词: 机械臂, 免疫原理, RBF神经网络, 逆运动学求解

Abstract: Neural network is applied to solving inverse kinematics of manipulator, whose weight is got through genetic algorithm or back propagated algorithm. These methods need to be further improved in solving accuracy and convergence rate. Through the generalization ability of artificial immune principle to RBF network data set training, the structure of hidden layer is adjusted and generated. When the structure is determined, recursive least squares method is used to determine weight value of network output, so as to adjust and learn the structure and weight value of the neural network self-adaptively. The simulation results verify that the trained neural network by the immune principle has better generalization ability and fast convergence. It can solve accuracy of manipulator inverse kinematics.

Key words: manipulator, immune principle, RBF neural network, solution of inverse kinematics