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Research of chaotic SVM with Incorporated Intelligence Algorithm forecasting model in SCM


Zhifang Liu, Guoqiao Ren, Xiaoting Shi


Vol. 6  No. 10  pp. 136-141


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


Forecasting activities, supply chain management, Lyapunov exponents, support vector machines, embedding dimension