Design And Research On Bank Loaning Risk Assessment And Warning RBF Neural Network System

It is always the most important for China's commercial banks to control loaning risk. According to limitation of traditional loaning risk assessment, this paper proposed a new evaluation of the risk of bank loans - based on even distributed combining of the RBFNN risk assessment and early risk warning assessment evaluation methods. At the same time, it use the object-oriented analysis and object-oriented design methods of bank loans risk assessment and early warning systems for the design. The main divided into the following phases:1. First of all, the analysis of bank loans evaluation and early warning based on the evaluation of the process of the project feasibility analysis, and then use OOA methods for require analysis, carried out a detailed analysis of all levels use cases.2. With outstanding bank loans risk assessment and early warning indicators on the basis of evaluation. The corporate lending risk evaluation index value and credit risk early warning indicators of value standards, the use of uniform design algorithm RBFNN access to the index system. Through examples of data for risk evaluation and warning evaluation, get the desired results.3. Use UML modeling, the bank loan risk assessment and early warning systems for the system design and detailed design, design a class diagram, the module structure of the system. And the modular design to a more detailed Sequence Diagram.4. In the system to achieve process, the use of Matlab functions and C # mixed technology, build Matlab Adaptive RBFNN function into COM components, in the process C # program to calling. At the same time application of a uniform design given the large number of samples were carried out risk assessment of the loan RBFNN training and evaluation of the early warning RBFNN training, after testing procedures, test satisfied with the training model. Loan evaluation of risk assessment and early warning, get the desired results. So that the program can run without Matlab software, and simplify the RBFNN code.With Matlab functions and C # mixed program so that our future in C # procedures can be used in Matlab software function of the large number of complex calculations and graphics transform and simplify the procedures for the preparation, at the same time giving full play to Matlab software powerful computing capabilities.In the past the bank risk early warning evaluation methods, the traditional fuzzy comprehensive evaluation method is not out of the decision-making process of random, subjective uncertainty and lack of self-learning ability to advance with the times of serious shortcomings. Used in the past RBF neural network evaluation method is implicit node fixed network, local extreme deficiencies. Although it has the advantages of self-learning ability, but the self-study training is a fatal drawback - in limited circumstances study samples of the lack of rigorous experimental design, so that by training hard to cover the vast majority of network performance risks, Evaluation of artificial neural network which can not achieve the expected performance requirements.We use the "uniform design" U1000 table, through the DSP software, access to large-scale standardized "uniform design" sample, and then use these to cover all of a representative sample space samples RBFNN adaptive training, training access to the test Error is less than the expected error model training results; examples of the test was scheduled less than the absolute value of 3.5, early warning levels in line with the actual effect. So as to establish a more accurate and correct, without missing, rapid bank lending risk assessment and early warning assessment model and evaluation methods.The design of the system laid the way for more than a platform for practical use, bank loans evaluation and early warning assessment of the value of practical use.