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Title: |
Research on the Pre-Warning Model of Enterprise Financial Crisis Based the Rough Sets and the Artificial Nerve Network with Particle Swarm Optimization |
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Source: |
International Conference on Engineering and Business Management (EBM 2010 PAPERBACK)
(pp 3870-3875)
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Author(s): |
Li-ning Zhang, Beijing institute of technology, achademy of mechanical-electrical engineering, Beijing, China 100081 ；North China institute of science and technology，civil engineering department, Beijing Yanjiao，China 101601
Qi zhang, Beijing institute of technology, achademy of mechanical-electrical engineering, Beijing, China 100081
Da-chao Lin, North China institute of science and technology，civil engineering department, Beijing Yanjiao，China 101601
jing An, North China institute of science and technology，civil engineering department, Beijing Yanjiao，China 101601 |
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Abstract: |
Abstract: The pre-warning of enterprise financial crisis is a hot studying spot in current theory field, and also becomes a considering focus in reality field. This paper firstly overviews the present research conditions of enterprise financial crisis pre-warning, sets up the pre-warning index system. Then makes reduction on the index system and finds the core unascertained factors which influence enterprise financial safety by using the Rough Sets(RS). On this basis, accomplishes the financial risk pre-warning based on the great nonlinear function approaching capability of artificial nerve network(ANN). Against for the shortage of ANN, this paper introduces the Particle Swarm Optimization(PSO), sets up a crisis forecast system to the enterprise financial crisis based on RS and ANN with PSO. Thus making the index system reduction, the dynamic study and induction of ANN, the Particle Swarm Optimization, the forecast and evaluation of enterprise financial crisis organically combined. At last, gives an actual example to justify the validity and efficiency of this model. This study supplies a new method for the dynamic pre-warning of enterprise financial crisis.
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