摘要
为解决传统最小二乘支持向量机采用交叉验证确定模型参数耗时长的问题,提出利用贝叶斯置信框架推断最小二乘支持向量机的模型参数．通过第1级推断确定最小二乘支持向量机的权矢量w和偏置项b,通过第2级推断确定模型的超参数μ和ζ,通过第3级推断的模型对比自动选择核函数的系数。根据时用水序列具有周期性和趋势性的特点,建立了基于贝叶斯推断最小二乘支持向量机的时用水量模型,实例分析结果表明,与基于传统最小二乘支持向量机和基于BP网络的预测模型相比,基于贝叶斯最小二乘支持向量机的时用水量预测模型的建模速度更快,预测精度更高。
As traditional least squares support vector machine（LSSVM） model parameters determined by crossvalidation is time-consuming, the Bayesian evidence framework was proposed to infer the LSSVM model parameters, The weight vector w and the bias term b of the LSSVM were obtained on the first level of inference. The model hyperparameters μ,ξ were determined on the second level of inference. The model comparison was performed on the third level of inference in order to automatically determine the coefficient of kernel function, According to the periodicity and trend of water demand series, an hourly water demand forecast model based on Bayesian LSSVM was established. Case analysis shows that the modeling speed of Bayesian LSSVM-based hourly water demand forcast model is faster and forcasting precision is higher than those of traditional LSSVM-based model and BP neural network-based model.
出处
《天津大学学报》
EI
CAS
CSCD
北大核心
2006年第9期1037-1042,共6页
Journal of Tianjin University(Science and Technology)
基金
国家自然科学基金(50078048).
关键词
贝叶斯推断
最小二乘支持向量机
供水系统
时用水量预测
Bayesian inference
least squares support vector machine
water distribution network
hourly water demand forcast
