Application of Chaos Particle Swarm Optimized Neural Networks for Evaluating the Credit Risk

Abstract

This paper proposes a hybrid algorithm based on chaos optimization and particle swarm optimization (PSO) to improve the performance of the neural networks (NN) on evaluating credit risk. The hybrid algorthm not only maintains the advantage of simple structure, but also improves the convergence of the traditional PSO algorithm, and enhances the global optimization capability and accuracy of the algorithm. The test results indicate that the performance of the proposed model is better than the ones of NN model using the BP algorithm and traditional PSO algorithm.