Abstract:

Gray neural network in the field of artificial intelligence prediction has been applied widely, but it has such problems as the slow speed of convergence, and local minimum, so its forecast precision is limited partly. This paper, in view of its defects, proposes the learning algorithm of the BP neural network optimized by PSO(Particle swarm algorithm). On the basis of this algorithm, grey prediction is used to make a preliminary forecast for the stock index futures’ historical data, and the results of initial forecasts are used as the input of the optimized BP neural network to be forecast and trained. A PSObased Combined forecasting Grey Neural Network model(PSOGMNN) is built. Finally, the simulation experiment result indicates that the prediction accuracy of the new prediction model is higher than that of the BP neural network, the gray neural network and the gray prediction model. It also shows the effectiveness and feasibility of the method.

Key words: BP neural network;particle swarm optimization;grey;grey neural network;PSOGMNN