Aiming at the challenging problems of the deficient accuracy and generalization ability of the furnace temperature prediction model when the municipal solid waste(MSW)incineration process data has abnormal val-ues and high dimensionality of feature variables,a robust weighted heterogeneous feature ensemble modeling meth-od is proposed to establish the furnace temperature prediction model of the municipal solid waste incineration pro-cess.Firstly,the high dimensional feature variables are divided into heterogeneous feature sets according to the in-cineration process mechanism,and the contribution of each heterogeneous feature set is evaluated by the mutual in-formation and correlation coefficient.Secondly,a robust stochastic configuration network(SCN)with the t mixture distribution is employed to construct base models,and penalty weights of training samples are determined at the same time.Finally,the robust weighted negative correlation learning(NCL)strategy is used to realize the syn-chronous training of base models.Comparative experiments are carried out using the historical furnace temperature data of a municipal solid waste incineration plant in China.The results show that the furnace temperature predic-tion model established by the proposed method performs more favourably in accuracy and generalization.
关键词
城市固体废物焚烧/炉温预测/异构特征集成/鲁棒建模/随机配置网络
Key words
Municipal solid waste incineration/furnace temperature prediction/heterogeneous feature ensemble/ro-bust modeling/stochastic configuration network(SCN)
维普
万方数据