Risk prediction method and device based on nucleus limit learning machine

Abstract

The present invention is applicable to the field of computers, and provides a method and device for risk prediction based on nuclear extreme learning machines, aiming to solve the problem of inability to determine the optimal value of penalty coefficient and kernel width of nuclear extreme learning machines in the prior art, resulting in risk The problem of low prediction accuracy. The method includes: obtaining the operating data of a predetermined number of enterprises; standardizing the operating data; optimizing the penalty coefficient and kernel width of the kernel extreme learning machine by using the gray wolf algorithm to obtain the optimized penalty coefficient and kernel width; based on the optimized penalty coefficient Construct a prediction model of the kernel extreme learning machine with the kernel width; carry out risk prediction according to the prediction model. Through the technical solution of the present invention, the gray wolf algorithm is integrated into the kernel extreme learning machine to determine the optimal value of the penalty coefficient and the kernel width, build a more accurate prediction model, realize effective prediction of risks, improve prediction accuracy, and assist in It has important application value in the scientific, reasonable and effective prediction of business risk by financial institutions.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a risk prediction method and device based on a nuclear extreme learning machine. Background technique [0002] In order to reduce the losses of financial institutions caused by business risks, especially bankruptcy risks, establishing a safe and effective risk warning mechanism and predicting business risks is an effective way for financial institutions to maintain investment returns. [0003] At present, the existing business risk prediction methods can be mainly divided into two categories, namely methods based on statistical models and methods based on artificial intelligence. The prediction methods based on statistical models mainly include univariate analysis, multivariate discriminant analysis, Logist regression model and factor analysis. Compared with forecasting methods based on statistical models, forecasting methods based on artificial intelligence are widely used in...

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Problems solved by technology

[0006] The purpose of the present invention is to provide a method and device for risk prediction based on nuclear extreme learning machine, aiming at solving the problem of inability to determine the optimal value of penalty coefficient and kernel width of nuclear extreme learning machine in the prior art, resulting in accurate risk prediction low degree problem

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