Comparison of Artificial Neural Networks Algorithms on Datasets with Different Characteristics

This paper deals with the research of different artificial neural network algorithms and their application on data sets with different characteristics. In the first part of the paper, a description of six neural network algorithms is given, on the one hand, and the characteristics of data sets measured through meta-features, on the other hand. The empirical part of the paper describes the development of the predictive models through the process of data preparation for modeling, hyperparameters optimization, and analysis and empirical comparison of the algorithms’ performance on different data sets. The research results show differences in the performance of the algorithms: Adam algorithm and its modifications have better performance than the AdaGrad algorithm and the basic gradient descent algorithm.