Robust width learning system

Abstract

A robust width learning system collects training data and performs linear conversion processing on the training data; solving an extended input matrix by using the input data matrix and the enhanced node matrix, solving an iterative initial connection weight matrix by using a ridge regression algorithm, and solving a residual matrix by using a residual formula; obtaining a residual probability density function by using a kernel density estimation algorithm, and calculating a weight matrix formed by all training data; and solving the connection weight matrix of the kth iteration, and if the maximum value of the absolute value of the difference between the output weights of two adjacent steps is not greater than a set threshold value or the number of iterations reaches a preset maximum number of iterations, ending the iteration, stopping the training of the model by the robust width learning system, and establishing a robust width learning system model. According to the system, the robustness of a width learning system can be improved, the problem of adverse effects on modeling precision caused by outliers can be effectively suppressed, and a robust width system model can be conveniently established so as to be suitable for prediction of related indexes in a complex industrial process.

Description

technical field [0001] The invention belongs to the technical field of industrial process modeling, and in particular relates to a robust width learning system. Background technique [0002] The control and optimization of complex industrial processes has always been a hot research direction, and the establishment of accurate models of complex industrial processes is the premise and basis of control and optimization. Mechanism modeling is based on the analysis of the physical and chemical mechanisms of the process, and derives the functional relationship between the operating variables, the state variables and the output variables. Mechanism modeling can accurately express the relationship between variables, effectively explain objective phenomena, and will not violate common sense, but its modeling is difficult, the modeling cycle is long, and researchers need to understand all relevant theoretical knowledge. In the past many years, data-driven modeling technology has attr...

AI Technical Summary

Problems solved by technology

[0006] In view of the problems existing in the above-mentioned prior art, the present invention provides a robust width learning system, which can improve the robustness of the width learning system, and can effectively suppress the adverse effects on modeling accuracy caused by outliers , it is convenient to establish a robust width system model, which is suitable for the prediction of relevant indicators in complex industrial processes

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