An RBF neural network based method for stocks market modeling and forecasting is presented,and a hierarchical genetic algorithm is proposed to train network parameters such as RBF centers,widths,connection weights and the configuration.

This paper presents a method for stock market modeling and forecasting based on fuzzy neural network, genetic algorithm is used to learn the connection weights of the fuzzy neural network and partitions of fuzzy subsets.

Based on the historical data,a forecast modeling method for structural equation model was discussed,where the future relationship between the system factors was described without future sample.

Aiming at a hard problem to crack for the forecast modeling for flight accident rates,the forecast model of flight accident rates is established by using of the LS-SVM method based on statistical learning theory.

Predictive Modeling Method of Gini Coefficient and Its Application in Beijing Economic Development Analysis;

Traditional approach to predictive modeling of large-scale sequential curves is to build model separately according to every curve,which causes heavy and complicated modeling work inevitably.

A new machine learning method—support vector machine (SVM) is used tobuild forecast models on the nonstationary time series in this paper, and theapplication in weather prediction field by this method is tested and analyzed aswell.