Rapid convergence algorithms for weight values updating based on BP network
基于BP网络的权值更新快速收敛算法

To solve the slow convergence of standard learning algorithm in BP network, two rapid convergence algorithms were suggested for weight values updating. One is rapid transmission algorithm based on gradient change rate. The other is flexible transmission algorithm based on gradient orientation. The two algorithms were simulated and compared in Game Style Training System for Mine Accident Rescuing. Here the algorithms would help game roles learn to estimate the danger degree according to ingredients of mine air, and then help trainees or biorobots take corresponding actions. The simulating results show that shorter convergence time is taken for the two algorithms than the standard algorithm.