Multi-response parameter optimization method based on radial basis function neural network prediction model

Abstract

The invention provides a multi-response parameter optimization method based on a radial basis function neural network prediction model and improved WPCA (weighted principal component analysis). According to the method, a non-linear prediction model of a production process is built by adopting a radial basis function neural network, capacity prediction indexes of the neural network model are introduced, a WPCA algorithm is adjusted, response with high prediction capacity receives priority in improvement in multi-response parameter design, and the optimization effect of technological parameters is improved. The WPCA generally adopts linear regression to establish a relation model between a response variable and a controllable factor variable in the multi-response parameter optimization design, however, the fitting degree of a linear regression model is not high for a complicated non-linear production process, and modeling requirements for parameter design cannot be met. The method is applied to the multi-response parameter optimization design of a thermal polymerization process of an aluminum-metallized polypropylene film capacitor, so that a satisfying comprehensive optimization effect of two responses of capacitance and loss tangent value of the capacitor is realized.

Description

technical field [0001] The invention relates to process control of product production, more specifically, to a method for optimizing multi-response parameters of production technology. Background technique [0002] With the complexity of the production process and the improvement of product quality requirements, the product optimization design process often needs to consider multiple quality characteristics. The multi-quality characteristic parameter optimization design method aims to greatly improve product quality through the optimization of process parameters. In actual production, multi-quality characteristic optimization design plays an increasingly important role in the process of continuous quality improvement. Quality loss function method and satisfaction function method are widely used in multi-response optimization design, but they ignore the correlation between each response. In the optimization design of multi-quality characteristics, if the correlation problem ...

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Problems solved by technology

[0004] The present invention proposes an improved principal component analysis method based on radial basis function neural network prediction, optimizes the design of multi-response parameters in the production process, and solves more and more highly complex problems in the modern advanced manufacturing process in the prior art In the nonlinear production process, the first-order and second-order polynomial parameter response surfaces cannot meet the requirements of establishing process models, etc.

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