ADAPTIVE DOUBLE NEO-FUZZY NEURON AND ITS COMBINED LEARNING

Abstract

The subject of the study in the article is the process of data classification under conditions of fuzziness and a limited volume of training sample. The goal is to enhance the double neo-fuzzy neuron within the framework of solving the data classification task with constraints on the training sample volume, processing time, as well as fuzziness and nonstationarity of input data. The tasks include improving the double neo-fuzzy neuron to enhance the system's approximation properties and developing a combined system learning method to ensure fast performance in an online mode. The approaches used are lazy learning, supervised learning, and self-learning. The following results have been obtained: the double neo-fuzzy neuron has been modified by introducing a compressive activation function at the output, creating conditions for building a neo-fuzzy network capable of adapting to non-stationary input data in an online mode and avoiding the vanishing gradient problem. Conclusion. A combined learning method for the double neo-fuzzy neuron has been proposed, involving parallel utilization of lazy learning, supervised learning, and self-learning with the "Winner Takes All" rule, followed by automatic formation of membership functions, enabling fast online classification in the presence of outliers in the input data.