Credit Risk Prediction Study Based on Modified Particle Swarm Optimized Fuzzy Neural Networks
In last decade, neural networks (NNs) have been proposed to predict credit risk because of their advantages of treating non-linear data with self-learning capability. However, the shortcoming of NNs is also significant due to a “black box” syndrome. Moreover, in many situations NNs more or less suffer from the slow convergence and occasionally involve in a local optimal solution, which strongly limited their applications in practice. To overcome NN’s drawbacks, this paper presents a hybrid system that merges fuzzy neural network and niche evolution particle swarm optimization into a comprehensive mode, named as niche evolution particle swarm optimization fuzzy neural network (NEPSO-FNN), the new model has been applied to credit risk prediction based on the data collected from a set of Chinese listed corporations, and the results indicate that the performance of the proposed model is much better than the one of NN model using the cross-validation approach.
Z. B. Xiong "Credit Risk Prediction Study Based on Modified Particle Swarm Optimized Fuzzy Neural Networks", Advanced Materials Research, Vols. 108-111, pp. 1326-1331, 2010