Data classification exists in various practical applications, such as the classification of words in natural language processing, classification of meteorological conditions, classification of environmental pollution degree, and so on. Artificial neural network is a basic method of data classification. A reasonable optimization algorithm will get better results for a loss function in the neural network. The research and improvement of these optimization algorithms has been a focus in this field. Because of the various optimizers developing in building the neural networks, an improved NAdam Algorithm (RNAdam) is proposed in this paper, on the basis of discussing and comparing several Algorithms with Adam Algorithm. This algorithm not only combines the advantages of RAdam algorithm, but also keeps the convergence of NAdam algorithm. A classification experiment is carried out on the data set composed of 300 sample points generated by the Make moon function. The experimental results show that the RNAdam algorithm is better than SGDM, Adam and Nadam algorithm in terms of the loss and accuracy between the output and the actual results, when the data are classified by the three-layer neural network. Therefore, the classification effect will be improved when this algorithm is applied to neural network for various practical data classification problems.
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