摘要：Development of financial markets and consequences of economic crises at international level caused effects on job environment and the companies’ future financial situation is a vital factor for different beneficiary groups, bankruptcy prediction can be used a mean to help them. Prediction methods are constantly evolving, and artificial neural networks have nowadays found a special position among these methods. Since learning constitutes a significant part of neural network models, learning methods of training these models are of particular importance. Therefore, finding a proper training method to reach the desired goals is necessary. Thus, this study seeks to find a better method of building and training artificial neural networks which leads to more accurate predictions of bankruptcy. Meanwhile, three neural networks of radial basis function type were built and trained separately by Altman model (1983), Zmijewski model (1984) and combinatory models’ variables. After evaluating the ability of these three models of bankruptcy prediction, their accuracy has been compared. Time span of 2004 to 2012 (eight years) has been used to select samples from the listed companies in Tehran Stock Exchange. Results show that all three models have the ability of predicting bankruptcy and the model trained with Altman Model’s variables is more accurate than the other two models in this regard.
