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

Authors:
Fu-Yuan Huang
Affiliations:
-
Venue:
PEITS '08 Proceedings of the 2008 Workshop on Power Electronics and Intelligent Transportation System
Year:
2008

Citing 0
Cited 2

A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices

Applied Soft Computing
An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting

International Journal of Applied Evolutionary Computation

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Abstract

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.