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A particle swarm optimized Fuzzy Neural Network for bankruptcy prediction

Since the excellent performances of treating nonlinear data with self-learning capability, the neural networks (NNs) are wildly use in financial prediction problem. But the NNs more or less suffer from the slow convergence, “black-box” i.e., it is almost impossible to analysis them for how they work. The Fuzzy Neural Networks(FNN) allow to add rules to neural networks. This avoids the black-box but lacks of effective learning capability. To overcome these drawbacks, in this study a particle swarm optimization algorithm is proposed first, then combined with the fuzzy neural network to predict corporation bankruptcy. The results indicate that the predictive accuracies obtained from PSO-FNN are much higher than the ones obtained from NNs. To make this clearer, an illustrative example is also demonstrated in this study.