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发表时间：2022-10-10

发表刊物：吉林大学学报 工学版

所属单位：电子信息与电气工程学部

期号：4

页面范围：858-864

ISSN号：1671-5497

摘要：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.

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