Improved Nelder-Mead Optimization Method in Learning Phase of Artificial Neural Networks
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Artificial neural networks method is the most important/preferred classification algorithm in machine learning area. The weightson the nets in artificial neural directly affect the classification accuracy of artificial neural networks. Therefore, finding optimum values ofthese weights is a difficult optimization problem. In this study, the Nelder-Mead optimization method has been improved and used fortraining of artificial neural networks. The optimum weights of artificial neural networks are determined in the training stage. Theperformance of the proposed improved Nelder-Mead-Artificial neural networks classification algorithm has been tested on the mostcommon 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 networksclassification algorithm. As a result of this comparison, the proposed improved Nelder-Mead-Artificial neural networks classificationalgorithm has given best results in all datasets.
SourceInternational Journal of Intelligent Systems and Applications in Engineering