Risk rating method for optimizing Hopfield neural network based on firefly algorithm
A technology of firefly algorithm and neural network, which is applied in the field of risk rating based on firefly algorithm to optimize Hopfield neural network, which can solve the problem of poor network learning speed and performance, particle swarm algorithm falling into local extremum region, genetic algorithm encoding and decoding variation and other problems, to achieve powerful nonlinear mapping and parallel computing capabilities, strong local and global optimization performance, and high robustness
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[0112] see figure 1 , the present invention provides a technical solution:
[0113] A risk rating method based on the firefly algorithm to optimize the Hopfield neural network, including the following six steps:
[0114] S1. Determine the performance period and risk level, extract modeling sample customers, and obtain customer data as a modeling index system. The customer data includes risk level and credit data affecting repayment performance;
[0115] S2. Preprocess the collected credit data, including missing value processing, outlier elimination and data standardization, and divide the training set data and test set data in chronological order;
[0116] S3, extracting credit data features and corresponding risk levels from the sample data in step S2, determining the input and output of the Hopfield neural network according to the sample features, and building a Hopfield neural network model;
[0117] S4. Construct the mapping relationship between the weight threshold of...
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