Thermal process soft sensor modeling method based on least squares and support vector machine ensemble

The invention discloses a thermal process soft sensor modeling method based on least squares and support vector machine ensemble, and belongs to the technical fields of thermal process and artificial intelligence intersection. The method includes selecting auxiliary variables as an input of a model and key variables to be predicted as an output of the model, selecting running data as an initial training sample, utilizing the soft fuzzy c-means clustering (SFCM) method to divide the initial sample into sub-datasets which are overlapped and which are provided with differences, establishing individual models on each sub-dataset, and synthesizing predicted outputs of the individual models to obtain estimation of the key variable; aiming to optional new acquired sample xk, obtaining a corresponding predicted value. According to the thermal process soft sensor modeling method, the soft fuzzy C-means clustering method is adopted, predicting accuracy is improved by means of establishing integrated models, calculating of the models is easier, and calculating efficiency is improved; boundary samples are processed effectively, the process is convenient to implement, the key variable can be predicted accurately, and important significance is provided to optimized operation of the thermal process system.