Open AccessKent-PSO optimized ELM fault diagnosis model in analog circuits
Author(s) -
Zongpeng Liu,
Zhiwei Lin,
Chengji Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012053
Subject(s) - particle swarm optimization , fault (geology) , extreme learning machine , generalization , algorithm , filter (signal processing) , computer science , set (abstract data type) , artificial intelligence , mathematics , artificial neural network , computer vision , mathematical analysis , seismology , programming language , geology
Fault information in analog circuits is complex and diverse, so as to improve the accuracy of fault diagnosis, a Kent mapping and Particle Swarm Optimization (PSO) combined optimization Extreme Learning Machine (ELM) model is proposed. Firstly, the original data set of the circuit is normalized to obtain the fault data set. Secondly, Kent mapping is used to initialize the position of particles in the particle swarm, which makes the initial particle swarm more evenly distributed in the search space and enhances the global optimization ability. Third, aiming at the problem of the input weight and hidden layer bias generated randomly by the ELM are easy to lead to poor generalization ability, the Kent-PSO algorithm is used to optimize the input weight and hidden layer bias of ELM to obtain better and more stable ELM network parameters and improve the fault diagnosis ability. The diagnosis example of Sallen-Key bandpass filter shows that the proposed method has better fault diagnosis performance than PSO-ELM model.