Application of optimizing BP neural networks algorithm based on Genetic Algorithm - Ding Shifei; Su Chunyang (Proceedings of the 29th Chinese Control Conference, 2010)
Back-Propagation (BP) neural networks is one of most mature neural networks models. It has better self-learning, self-adapted, robustness and generalization ability and has been widely applied to pattern recognition, function approximation and image processing, etc. But for BP neural networks algorithm, the convergence rate is slow, it is easy to get stuck in a local minimum, and its structure is hard to designed. A lot of improved algorithms have been proposed to overcome these disadvantages, but these algorithms need more storage space and are not very brief. This paper does a research on the optimized BP neural networks based on Genetic Algorithm(GA), firstly, the optimized BP neural networks algorithm which optimizes the connection weights and structure at the same time with GA is established in order to overcome to get stuck in a local minimum. Secondly, the network structure is optimized and the number of hidden neurons can be determined automatically. At last the validity of the optimized BP algorithm is proved by analyzing an example.