A Modified Particle Swarm Algorithm Combined with Fuzzy Neural Network with Application to Financial Risk Early Warning
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 and occasionally involve in a local optimal solution. To overcome these drawbacks of PSO and FNN, in this study a modified particle swarm optimization algorithm (MPSO) is developed and then combined with neural network to optimize the network weight training process. Furthermore, the new MPSO-FNN model has been applied to financial risk early warning problem, and the results indicate that the predictive accuracies obtained from MPSO-FNN are much higher than the ones obtained from original FNN system. 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.
Huang Fu-yuan Li Rong-jun Liu Han-xia Li Rui
School of Business Administration, South China University of Technology Guangzhou city, Guangdong Province, 510640, P.R.China
国际会议
2006 Asia-Pacific Services Computing Conference(IEEE亚太地区服务计算会议)
广州
英文
168-173
2006-12-12（万方平台首次上网日期，不代表论文的发表时间）