基于粗糙集神經(jīng)網(wǎng)絡(luò)技術(shù)的公司財務(wù)危機預(yù)警研究.pdf

1、河北工業(yè)大學(xué)碩士學(xué)位論文基于粗糙集神經(jīng)網(wǎng)絡(luò)技術(shù)的公司財務(wù)危機預(yù)警研究姓名：周琳申請學(xué)位級別：碩士專業(yè)：管理科學(xué)與工程指導(dǎo)教師：仝凌云2010-11基于粗糙集神經(jīng)網(wǎng)絡(luò)技術(shù)的公司財務(wù)危機預(yù)警研究 ii RESEARCH ON COMPANY FINANCIAL CRISIS PREDICTION BASED ON ROUGH SET AND NEURAL NETWORK TECHNOLOG

2、Y ABSTRACT With the global economic integration of the big step and Our market economy has developed rapidly，Increasingly fierce competition among company, Market in which an unlimited business opportunities, surprises o

3、f the risks and crisis. Uncertainty and complexity of the Market economic environment are growing, This will probably make company in the financial crisis. Company in the financial crisis, not only endangers the company

4、itself to the survival and development and spread to the investors and the interests of all sectors of society. Therefore, building the necessary financial crisis early warning mechanism and to establish a powerful finan

5、cial crisis early warning system has very important actual significance. This paper introduces a number of domestic and international financial crisis early warning research of the relevant documents.Careful comparison

6、and analysis of the current financial crisis early warning method. At present, about the financial crisis early warning method of the study includes qualitative analysis methods and quantification analysis methods introd

7、uced after two categories. however，quantification analysis methods used most widely.quantification analysis methods mainly include univariate judge model, multiple linear judge model, logistic judge regression model and

8、ANN.However,research are mostly confined to static method. The text proposed based on Rough set assembly with the neural network of dynamic financial crisis early warning method. The method for the financial crisis early

9、 warning research provides new ideas, and can better informed and perfecting corporate financial crisis early warning of the theory and methods. Rough set theory and neural network combined with good synergy, has aroused

10、 extensive concern, both domestic and foreign scholars. This paper hope that combine the advantages and use of nerve network the processing ability of nonlinear systems, with coarse of knowledge about jane theory as a ne