Feature Subset Selection -based Fault Diagnoses for Automobile Engine
according to the stability of the membership degree and low identification rate in fuzzy inference system, this dissertation proposes the application of adaptive neural network-based fuzzy inference system to engine error diagnosis. To reduce the impact of excessive parameters on classification accuracy and cost, it also raises an asynchronous parallel particle swarm optimization method applied to the selection of feature subset. The method use uniform mutation operator, balancing effectively the particles ability to search globally and to develop. The asynchronous parallel particle swarm optimization algorithm(AP-PSO) that is used to select the feature subset is a potential feature subset that carries the characteristic of firstly initializing every particle as the selection question. Then, the method adopts improved asynchronous parallel particle swarm algorithm to conduct optimal searching based on the particle swarm that include several feature subsets and evaluate the classification ability (adaptive value) of the feature subset selected by way of Support Vector Machine. Finally, the optimal feature subset is got.Through verification of the build diagnosis model with data of engine tests, it has been found that the recognition accuracy attain to 98.72％, training error falling to 0.004423.The experiment indicates that the recognition rate of ANFIS system is significantly better than independent neural network reasoning system, fuzzy inference system.
adaptive neural fuzzy interference system fault diagnosis particle swarm optimization algorithm feature subset selection
Lifang Kong Shisong Zhu Zhe Wang
Xuzhou Air Force College Jiangsu Xuzhou 221008, China Xuzhou Air Force College Jiangsu Xuzhou 221002, China
国际会议
杭州
英文
747-750
2011-10-28（万方平台首次上网日期，不代表论文的发表时间）