在相关向量机的预测模型中，核函数的选择和核参数的取值对模型预测性能影响很大．针对这一问题，文中采用多属性高斯核函数建立相关向量机预测模型，提出了一
种自优化的核参数学习方法进行参数优化，并将该模型用于二维标准函数预测和污水处理系统出水水质预测，与不同核函数下的相关向量机模型以及不同参数优化方法下获得的预测模型进行了对比实验．结果表明，基于自优化的多属性高斯核相关向量机模型对低维数据的参数敏感度较低，对高维数据有良好的输出精度和稀疏性，在污水出水水质预测中获得比较满意的结果．

In relevance vector machine prediction models,the selection of kernel functions and the values of kernel parameters have great influence on the prediction performance of the models.Aiming at this issue,a novel relevance vector machine prediction model is constructed on the basis of multiple-attribute Gaussian kernel functions,and a self-optimal kernel parameter-learning method is proposed to optimize the kernel parameters.Then,the constructed model is used to predict a two-dimensional standard function as well as the effluent quality of a wastewater treatment system.Finally,this model is compared with several models using different kernel functions and several models using different parameter optimization methods by simulation experiments.The results indicate that the proposed model is less sensitive to lower dimension data and has better output accuracy and sparsity in dealing with higher dimension data,and that it shows a satisfying performance in predicting the effluent quality of wastewater.