Glass furnace temperature forecast method based on learning machine related to Gaussian mixture distribution

Abstract

The invention relates to a g lass furnace temperature forecast method based on a learning machine related to Gaussian mixture distribution, and belongs to the automatic control field, the information technology field, and the advanced manufacturing field. Modeling problems of glass furnace temperature forecast such as complicated glass furnace internal reaction process, complicated asymmetric noises of data, and input variables including time series variables, the glass furnace temperature forecast method based on the learning machine related to robustness in the Gaussian mixture distribution is provided. A kernel function regression model is used as a forecast model structure, and non-zero mean value Gaussian mixture distribution is used as probability density distribution of forecast model residual terms, and the time series variables are parallely arranged, and are used as the input variables of the models, and then a Bayesian inference method is used to acquire approximate posterior probability distribution of model structure parameters, and therefore the structure parameters of the forecast model is acquired. The glass furnace temperature forecast method is effectively used for the forecast of the glass furnace pool bottom temperature, and therefore a glass furnace control and operation optimization effect is improved.

Description

technical field [0001] The invention belongs to the fields of automatic control, information technology and advanced manufacturing, and specifically relates to the complex internal reaction process of the glass furnace, the presence of complex asymmetric noise in the data, and the continuous time series variables included in the input variables. To deal with such modeling problems, a glass furnace temperature prediction method based on mixed Gaussian distribution correlation learning machine is proposed. Background technique [0002] In the control and optimization process of the glass furnace production process, the forecast of the bottom temperature of the glass furnace plays a key guiding role. However, due to the fact that the actual industrial production process involves complex physical and chemical processes, the production process data is affected by the environment and measurement, there are strong uncertainties and complex asymmetric noise, and the input variables ...

AI Technical Summary

Problems solved by technology

However, due to the fact that the actual industrial production process involves complex physical and chemical processes, the production process data is affected by the environment and measurement, there are strong uncertainties and complex asymmetric noise, and the input variables contain continuous time series variables. The modeling method will have problems such as low prediction accuracy and poor generalization ability

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