MFL defect quantification framework based on combination of adaptive genetic algorithm and BP neural network

In order to overcome the disadvantages of BP neural network based on traditional genetic algorithm, such as slow training speed, low quantitative accuracy, based on improving the computing method of the rate of crossover and mutation, also taking current stage of the population's evolution into consideration, an improved adaptive genetic algorithm is introduced. This new algorithm is used to optimize BP network's initial weights, and the defect features from magnetic flux leakage are used to train the network, so as to achieve the quantification of length, width, depth of the defects. With this model, the training time of net work can be saved and the computing accuracy can be improved as well.