Using adaptive ant colony algorithm optimized BP neural network to identify the DGA fault

The BP network uses the rule of local decline to slow convergence speed and easy to fall into local optimum. With the increase of BP input parameters lead to the dimension disaster. Taking into consideration the above problems, Using adaptive ant colony optimization algorithm optimized the training process of BP network referred to as AACOABP. The BP network and the BP network optimized by adaptive ant colony algorithm to (AACOABP) were used to identify the faulty type of the collecting samples about DGA fault test data, AACOABP can make the fault identification accuracy is greatly increased, the recognition accuracy from 50% ~ 75% to 87.5% ~ 100%.