Improved BP neural network model and its stability analysis

As for shortcomings of classical BP algorithm such as bad anti-jamming ability, low learning rate and easy plunging into local minimum, a new kind of improved BP algorithm was proposed with varying slope of activation function and dynamically adjusting different learning rates. Moreover, the convergence of this improved algorithm was analyzed based on the principle of Lyapunov stability. Considering the deficiency of network training and insufficient learning rate, a new composite error function was invented. A new method of dynamic adjustment of different learning rate was adopted to accelerate the convergence of classical BP algorithm, and to avoid plunging into the local minimum point. The proposed algorithm was applied to the inspection of the surface defective image of steel strips and compared with traditional algorithm with defect detection performance parameters. The results show that the improved BP algorithm has many merits such as high inspection speed, high discrimination and real-time capacity which can satisfy the demand of defect detection on steel plate surface, so it is an effective method.