The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine

Authors:
Qi Wu
Affiliations:
School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu 210096, China and Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry o ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Aiming at the complex system with multi-dimension, small samples, nonlinearity and multi-apex, and combining chaos theory, genetic algorithm with support vector machine (SVM), a kind of chaotic SVM named Cv-SVM short for chaotic v-support vector machine is proposed in this paper. Cv-SVM, whose constraint conditions are less than those of the standard v-SVM by one, is proved to satisfy the structure risk minimum rule under the condition of probability. Moreover, there is no parameter b in the regression function of Cv-SVM. And then, an intelligence-forecasting method is put forward. The results of application in car demand forecasting show that the forecasting method based on Cv-SVM is feasible and effective.