期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2006
卷号：6
期号：10
页码：136-141
出版社：International Journal of Computer Science and Network Security
摘要：Forecasting activities are widely performed in the various areas of supply chains for predicting important supply chain management(SCM) measurements such as demand volume in order management, product quality in manufacturing processes, capacity usage in production management, traffic costs in transportation management, and so on. The accuracy of forecasting has a great influence on the efficiency of SCM. According to the chaotic and non-linear characters analyze of SCM data, the model of support vector machines (SVM) based on Lyapunov exponents was established. The time series matrix was established according to the theory of phase-space reconstruction, and then Lyapunov exponents was computed to determine time delay and embedding dimension. A new incorporated intelligence algorithm is proposed and used to determine free parameters of support vector machines. Subsequently, examples of demand glass panels for CRT TV data. The empirical results reveal that the proposed model outperforms the SVM model. BP algorithm was used to compare with the result of SVM. The results show that the presented method is feasible and effective
