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

作者：Wu, Qi^*; Wu, Shuyan; Liu, Jing

来源：Engineering Applications of Artificial Intelligence, 2010, 23(4): 487-494.

DOI：10.1016/j.engappai.2009.07.003

• Support vector machine
• Particle swarm optimization
• Adaptive
• Gaussian loss function
• Forecasting

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.

• 出版日期2010-6
• 单位东南大学; 上海海事大学