Integration of an Improved Particle Swarm Optimization Algorithm and Fuzzy Neural Network for Shanghai Stock Market Prediction

Particle Swarm Optimization (PSO) algorithm and Fuzzy Neural Network (FNN) system has been widely used to solve complex decision making problems in practice. However, both of them more or less suffer from the slow convergence,”black-box” and occasionally involve in a local optimal solution. To overcome these drawbacks of PSO and FNN, in this study an improved particle swarm optimization algorithm (IPSO) is developed and then combined with fuzzy neural network to optimize the network training process. Furthermore, the new IPSO-FNN model has been applied to Shanghai stock market prediction problem, and the results indicate that the predictive accuracies obtained from IPSO-FNN are much higher than the ones obtained from neural network system(NNs). To make this clearer, an illustrative example is also demonstrated in this study. It seems that the proposed new comprehensive evolution algorithm may be an efficient forecasting system in financial time series analysis.

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