A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal

A genetic algorithm based on the nested intervals chaos search (NICGA) has been given. Because the nested intervalschaos search is introduced into the NICGA to initialize the population and to lead the evolution of the population, the NICGA has theadvantages of decreasing the population size, enhancing the local search ability, and improving the computational efficiency and op-timization precision. In a multi-layer feed forward neural network model for predicting the silicon content in hot metal, the NICGAwas used to optimize the connection weights and threshold values of the neural network to improve the prediction precision. The ap-plication results show that the precision of predicting the silicon content has been increased.