Selective hierarchical integration Gaussian process regression soft measurement modeling method based on evolutionary multi-objective optimization

Abstract

The invention discloses a selective hierarchical integration Gaussian process regression soft measurement modeling method based on evolutionary multi-objective optimization. The method comprises the following steps: firstly, constructing a diversity input variable subset by combining Bootstrapping random resampling and partial mutual information; dividing the corresponding original sample subset into different modeling areas by using a Gaussian mixture model algorithm, establishing a corresponding Gaussian process regression base model, carrying out posteriori probability weighted fusion, constructing a first layer of integrated model EGPR, constructing a multi-objective optimization problem from the perspective of evolutionary optimization, and selecting an EGPR model which is good in performance and meets diversity for final integration. The diversity of sample information and input variable information is fully considered, and the diversity and prediction precision of the base modelcan be effectively ensured. Moreover, due to the introduction of the selective integration strategy, the defect that all local models are fused through traditional integrated learning is effectivelyovercome, the complexity of integrated modeling is remarkably reduced, and the model prediction performance is improved.

Description

technical field [0001] The invention relates to a method in the field of soft sensor technology, in particular to a selective layered integration Gaussian process regression soft sensor modeling method based on evolutionary multi-objective optimization. Background technique [0002] With the development of modern industrial manufacturing technology, accurate and real-time measurement of key parameters in the process and the implementation of optimal control have gradually become an important means to improve product quality. However, the actual industrial production process usually has the characteristics of strong nonlinearity and large hysteresis, which lead to long measurement periods for these parameters and are difficult to detect. Soft-sensing technology provides an effective way for online estimation of such difficult-to-measure parameters. [0003] Integrated learning completes learning tasks by constructing and combining multiple base models, and can often obtain si...

AI Technical Summary

Problems solved by technology

However, most of the existing selective integration methods do not consider the balance between the accuracy of the base model and diversity, resulting in poor performance of the integrated model.

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