关键词: 人工智能, 控制理论, 动态递归神经网络, 粒子群优化, 免疫系统, 超声马达

Abstract: A learning algorithm for dynamic recurrent Elman neural networks was presented, which is based on an immune particle swarm optimization (PSO). The algorithm computed concurrently the evolution of network structure, weight, initial inputs of the context units and the self-feedback coefficient of the modified Elman network. Thereafter, a novel control method based on the proposed algorithm was introduced and discussed. More specifically, a dynamic identifier was constructed to perform speed identification, and a controller was designed to perform speed control for Ultrasonic Motors (USM). Numerical experiments show that the identifier and the controller based on the proposed algorithm can both achieve higher convergence precision and convergence rate than those based on other state-of-the-art algorithms. In particular, the experiments show that the identifier can approximate the USM's nonlinear inputoutput mapping accurately. The effectiveness of the controller is verified using constant speed, step and sinusoidal changing speeds.

Key words: artificial intelligence, control theory, dynamic recurrent neural network, particle swarm optimization, immune system, ultrasonic motor