Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO

Authors:
Qi Wu;Shuyan Wu;Jing Liu
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 ...;Zhengzhou College of Animal Husbandry, Zhengzhou, Henan 450011, China;College of Information Engineering, Shanghai Maritime University, Shanghai 200135, China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

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Abstract

In view of the bad capability of the standard support vector machine (SVM) in field of white noise of input series, a new v-SVM with Gaussian loss function which is call g-SVM is put forward to handle white noises. To seek the unknown parameters of g-SVM, an adaptive normal Gaussian particle swarm optimization (ANPSO) is also proposed. The results of applications show that the hybrid forecasting model based on the g-SVM and ANPSO is feasible and effective, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than v-SVM and other traditional methods.