Abstract
To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.
Highlights
In recent years, neural networks (NNs), especially back-propagation NNs (BPNN), are developed and applied quickly to financial distress prediction because of their excellent performances of treating non-linear data with learning capability [1,2]
The new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach
A new genetic algorithm combined with neural network, named as Adaptive Genetic Fuzzy Neural Network (AGFNN), is presented to predict financial distress on the data collected from a set of Chinese listed corporations, and the results indicate that the performance of AGFNN model is much better than the ones of other NN models
Summary
Neural networks (NNs), especially back-propagation NNs (BPNN), are developed and applied quickly to financial distress prediction because of their excellent performances of treating non-linear data with learning capability [1,2]. A new genetic algorithm combined with neural network, named as Adaptive Genetic Fuzzy Neural Network (AGFNN), is presented to predict financial distress on the data collected from a set of Chinese listed corporations, and the results indicate that the performance of AGFNN model is much better than the ones of other NN models.
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