Application research based on Artificial Fish-swarm Neural Network

Sinter tumbler strength is an important parameter in the sintering process, and has an important influence on the performance of finished sinter. Artificial fish swarm algorithm have good ability to acquire the global performance, the neural network has strong nonlinear ability and local optimization performance,; AFSA+BP algorithm combined with artificial fish swarm algorithm and BP algorithm, realizes the complementary artificial fish swarm algorithm global search capability and BP algorithm’s local optimization combination of performance, an artificial fish swarm neural results show that the network combination algorithm, it is shown that comparing with the traditional BP neural network forecasting method, the presented forecasting method has better adaptive ability and can give better forecasting results. The artificial fish – swarm algorithm network is trained and checked with the actual production data this algorithm has strong generalization capability, predictive accuracy improved significantly, and speed up the convergence rate, provides an effective method for strength prediction. Which be used for off-line learning and prediction, a good basis for the online application.