Soft measurement method based on optimal selection and regression of orthogonal components

Abstract

The invention discloses a soft measurement method based on optimal selection and regression of orthogonal components in order to predict the orthogonal components conducive to quality indexes by meansof optimal selection and utilize the selected orthogonal components to create a soft measurement model for optimal regression. Specifically, according to the method, principle component analysis (PCA), independent component analysis (ICA) and an partial least squares algorithm (PLSR) are parallelly and separately utilized to obtain the corresponding orthogonal components at first, then adjacent component analysis based on a genetic algorithm is utilized to optimally select the orthogonal components, and finally, the selected orthogonal components are utilized to conduct optimal regression molding based on a particle swarm algorithm. Compared with a traditional method, the method has the advantages that characteristic components conducive to prediction of the quality indexes are selected in an optimal mode, and final predicted values of the quality indexes are obtained according to an optimal regression vector. Therefore, the soft measurement performance of the method is fully ensured,and the method is a more optimized soft measurement method.

Description

technical field [0001] The invention relates to an industrial soft-sensing method, in particular to a soft-sensing method based on optimal selection and optimal regression of orthogonal components. Background technique [0002] Maintaining the stability of an enterprise's product quality is the fundamental way to improve its market competitiveness and brand effect. These key variables that can directly or indirectly reflect product quality are usually obtained using online analysis methods or offline assay analysis methods. However, on-line analytical instruments are expensive and costly to maintain; while off-line assays require a long time to measure the corresponding data, resulting in a serious lag and unable to reflect the current quality status in a timely manner. In order to obtain product quality information in real time at low cost, soft sensor technology emerges as the times require. The basic idea is to use other easily measurable process variables related to the...

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Problems solved by technology

There are few research documents or patents that comprehensively consider the simultaneous use of these three types of algorithms, and they only stay at the level of improving a single algorithm

Secondly, not all of the orthogonal components extracted by PCR, ICR, and PLSR are beneficial to the prediction of quality index data, and there is no feasible solution for how to optimally select the orthogonal components that are helpful for quality prediction

Finally, when using orthogonal components to directly predict quality indicators, the common idea is the least squares regression method, which fails to find the relationship between input and output from the perspective of optimizing the regression coefficient vector

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