Research on Fault Diagnosis Based on BP Neural Network Optimized by Chaos Ant Colony Algorithm

In view or show:timings of BP neural network, which is slow to converge and tends to nap in local optimum when applied in fault diagnosis. an approach for fault diagnosis based on BP neural network optimized by chaos ant colony algorithm is proposed Mathematical model of chaos ant colony algorithm is created Real-coded method is adopted and the weights and thresholds of BP neural network are taken as ant space position searched by chaos ant colony algorithm to train BP neural network naming result of chaos ant colony algorithm is compared with that of conventional BP algorithm and from both results it is can be seen that chaos ant colony algorithm can overcome the shortcomings of BP algorithm It is proved that mathematical model of chaos ant colony algorithm is correct and optimization method is valid through experimental simulation for machinery fault diagnosis of mine ventilator