Improved Robot Control Method Based on Fuzzy RBF Neural Network

Analyzing robotic systems with single-degree-of-freedom operation through neural network algorithms has been a hot research topic in recent years. The fuzzy RBF neural network is a three-layer forward network, which realizes the mapping from the input layer space to the output layer space through the nonlinear transformation from the input layer space to the implied layer space and the linear transformation from the implied layer space to the output layer space. The learning of the parameters of these two interlayer transformations can be carried out separately, which makes the fuzzy RBF neural network faster and avoids the local minima problem. In this paper, fuzzy RBF neural network is used to control established intelligent robot model, and the control system is simulated and analyzed. The results show that the fuzzy RBF neural network algorithm can improve the control accuracy and real-time performance of intelligent robots.