For this project, a total of 1350 group company' s financial position data, each data input is 10-dimensional, on behalf of the above 10 indicators, output is 1-dimensional, on behalf of the Company' s financial position, financial condition and 1 is for- 1 indicates that the financial position of the problems. 1000 data from randomly selected as the training data, 350 sets of data as test data. According to the data dimension, using BP neural network structure is 10-6-1, trained a total of 10 BP neural network to generate weak classifiers, and finally with 10 weak classifiers strong classifier to classify the company' s financial condition.

financial early warning model\Bp_Ada_Fore.m
.............................\Bp_Ada_Sort.m
.............................\data.mat
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financial early warning model

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