关键词: 信息处理技术, 信息熵, 特征提取, 改进离散粒子群优化算法, 容差电路

Abstract: The fault information entropy is used to describe the fault symptoms of output signal of tolerance circuit. Three different feature subset selection rules are adopted, such as equal interval-selected feature point, and feature points selected by maximum information entropy of single feature points and joint information entropy of multiple feature points. Four kinds of improved basic particle swarm optimization (BPSO) algorithms are proposed to search the fault feature subsets. These four algorithms are improved mapping function BPSO algorithm, adaptive weighting BPSO algorithm, natural selection-based BPSO algorithm, and BPSO algorithm based on natural selection and adaptive weighting. The optimal feature subsets obtained by feature extraction are used to train the neural networks as classifier. The fault location of tolerance circuit is completed using a trained neural network . Experimental results show that the optimal feature subset searching methods based on improved BPSO algorithm are valid, and the accuracy of fault location can reach 95.2%.

Key words: information process technology, information entropy, feature extraction, improved basic particle swarm optimization aglorithm, tolerance circuit