Improved Nelder-Mead Optimization Method in Learning Phase of Artificial Neural Networks

Özet

Artificial neural networks method is the most important/preferred classification algorithm in machine learning area. The weights on the nets in artificial neural directly affect the classification accuracy of artificial neural networks. Therefore, finding optimum values of these weights is a difficult optimization problem. In this study, the Nelder-Mead optimization method has been improved and used for training of artificial neural networks. The optimum weights of artificial neural networks are determined in the training stage. The performance of the proposed improved Nelder-Mead-Artificial neural networks classification algorithm has been tested on the most common datasets from the UCI machine learning repository. The classification results obtained from the proposed improved Nelder-Mead- Artificial neural networks classification algorithm are compared with the results of the standard Nelder-Mead-Artificial neural networks classification algorithm. As a result of this comparison, the proposed improved Nelder-Mead-Artificial neural networks classification algorithm has given best results in all datasets.