Risk prediction method and device based on nucleus limit learning machine

A nuclear extreme learning machine and risk prediction technology, which is applied in the computer field, can solve the problems of low risk prediction accuracy and the inability to determine the optimal value of the penalty coefficient and kernel width of the nuclear extreme learning machine, so as to achieve good search ability and improve The effect of precision

Inactive Publication Date: 2016-10-12
WENZHOU UNIVERSITY
View PDF0 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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 inabil...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Risk prediction method and device based on nucleus limit learning machine
  • Risk prediction method and device based on nucleus limit learning machine
  • Risk prediction method and device based on nucleus limit learning machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0027] figure 1 It is a flow chart of a risk prediction method based on a nuclear extreme learning machine provided in Embodiment 1 of the present invention, specifically including steps S101 to S104, which are described in detail as follows:

[0028] S101. Obtain operating data of a predetermined number of enterprises, where the operating data includes a predetermined number of feature values ​​of attribute features.

[0029] Specifically, enterprise operating data refers to the attribute characteristics of the current economic situation of the enterprise defined from the perspective of accounting. The attribute characteristics represent a series of financial ratios, which can specifically include but are not limited to cash / current liabilities (cash / current liabilities), Assets ratio (cash / total assets), current assets / current liabilities ratio (current assets / current liabilities), current assets / total assets ratio (current assets / total assets), working capital / total assets ...

Embodiment 2

[0045] figure 2 It is a flowchart of a risk prediction method based on a nuclear extreme learning machine provided in Embodiment 2 of the present invention, specifically including steps S201 to S208, which are described in detail as follows:

[0046] S201. Acquire operating data of a predetermined number of enterprises, where the operating data includes a predetermined number of feature values ​​of attribute features.

[0047] Specifically, enterprise operating data refers to the attribute characteristics of the current economic situation of the enterprise defined from the perspective of accounting. The attribute characteristics represent a series of financial ratios, which can specifically include but are not limited to cash / current liabilities (cash / current liabilities), Assets ratio (cash / total assets), current assets / current liabilities ratio (current assets / current liabilities), current assets / total assets ratio (current assets / total assets), working capital / total assets...

Embodiment 3

[0093] image 3 It is a schematic structural diagram of an apparatus for risk prediction based on a kernel extreme learning machine provided in Embodiment 3 of the present invention. For convenience of description, only the parts related to the embodiment of the present invention are shown. image 3 An example apparatus for risk prediction based on a kernel extreme learning machine may be the subject of execution of the method for risk prediction based on a kernel extreme learning machine provided in Embodiment 1 above. image 3 The example centralized risk prediction device based on the kernel extreme learning machine mainly includes: a data acquisition module 31 , a standardization module 32 , a gray wolf optimization module 33 , a model construction module 34 and a prediction module 35 . The detailed description of each functional module is as follows:

[0094] A data acquisition module 31, configured to acquire business data of a predetermined number of enterprises, the b...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/04G06Q10/06
CPCG06Q10/04G06Q10/0635
Inventor 陈慧灵赵学华王名镜童长飞蔡振闹李俊沈立明王科杰朱彬磊
Owner WENZHOU UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap