Adaptive modeling method for support vector regression based on KKT condition and nearest neighbor method

Abstract

The invention discloses an adaptive modeling method for support vector regression based on KKT condition and a nearest neighbor method in the field of machine learning. The method comprises the followThe invention discloses an adaptive modeling method for support vector regression based on KKT condition and a nearest neighbor method in the field of machine learning. The method comprises the following steps: constructing a mould and learning according to sample sets acquired by an off-line steady state test, standardizing newly-added samples, and judging whether the newly-added samples meet theing steps: constructing a mould and learning according to sample sets acquired by an off-line steady state test, standardizing newly-added samples, and judging whether the newly-added samples meet the KKT condition; establishing a rolling time window; computing the related similarity according to a sample most similar with the newly-added samples, defining a threshold value and an upper limit valuKKT condition; establishing a rolling time window; computing the related similarity according to a sample most similar with the newly-added samples, defining a threshold value and an upper limit value and a lower limit value compared with the threshold value, and comparing the threshold value with the upper limit value and the lower limit value; and performing corresponding adjustment on the sampe and a lower limit value compared with the threshold value, and comparing the threshold value with the upper limit value and the lower limit value; and performing corresponding adjustment on the sample sets according to a comparison result and correcting the model until all newly-added samples are processed. The method ensures that a regression mould makes full use of historic training results, cle sets according to a comparison result and correcting the model until all newly-added samples are processed. The method ensures that a regression mould makes full use of historic training results, can obviously reduce the subsequent training time, and greatly contribute to industrial production, in particular implementation of on-line soft measurement of parameters during the power station boilean obviously reduce the subsequent training time, and greatly contribute to industrial production, in particular implementation of on-line soft measurement of parameters during the power station boiler combustion.r combustion.

Description

technical field [0001] The invention relates to a modeling method, in particular to a support vector regression machine adaptive modeling method based on KKT conditions and the nearest neighbor method, and belongs to the field of machine learning modeling. Background technique [0002] Machine learning (Machine Learning) is the means and mechanism of acquiring knowledge from known sample data or information through mining, induction, deduction, analogy, etc. It is another important research field of artificial intelligence application after the expert system, and has caused received widespread attention. The purpose of machine learning is to learn the training samples given in advance according to a certain method or algorithm designed, and then obtain an estimate of the dependence between the input and output of a certain system, and make the estimate better for the unknown output. To make predictions or judgments about their nature as accurately as possible. Support Vect...

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Problems solved by technology

[0004] At present, there is still a problem of online training and updating of the model obtained through soft sensing, mainly because many thermal problems are gradual problems (such as combustion equipment aging, replacement or changes in certain working conditions), accompanied by on-site data With the continuous accumulation and improvement of samples, new samples are also emerging and increasing. The information carried by these new samples is different from the original samples or test samples. At this time, the obtained static model will not be able to reflect the actual operation. change of situation

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