Gradient genetic algorithm-based performance fault diagnosis model

Abstract

This paper, in order to reduce fault and improve ratio of recognition, build adaptive neural network-based fuzzy inference system (ANFIS), which was applied to build a fault diagnosis model of automobile engine, adopts the method of information fusion in entropy method to optimize the input interface. 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. By using gradient descent genetic algorithm and optimization of system parameters of neutral network learning algorithm, so as to speed up learning. Through verification of the build diagnosis model with data of engine tests, it has been found that the recognition accuracy attain to 97.39%, training error falling to 0.001702. The experiment indicates that gradient descent genetic algorithm is a fast algorithm that can support the local optimization of individual chromosome and the global optimization of chromosomes in a group.