Analog circuit fault diagnosis based on differential evolution and extreme learning machine
Zhou Jiangma · Computer Engineering and Applications Journal · 2014
Extreme learning machine has quick learning speed and high accuracy. To improve the generalization performance, differential evolution algorithm, which has the features of global convergence and easy computation, is introduced in the parameter optimization of extreme learning machine. A parameter optimization model based differential evolution algorithm is established in order to combine the advantages of the algorithms, applied in analog circuit fault diagnosis. Firstly, the output voltage signals from the test nodes of all analog circuit are obtained and the fault feature vectors are extracted from principal component analysis. Then, using differential evolution algorithm global optimization ability encodes the connection weights and thresholds to get the optimal structure, achieving good generalization performance and robustness. This approach applied in diagnosis is more effective and can get satisfied results in a short time.