АДАПТИВНИЙ ПОДВІЙНИЙ НЕО-НЕЧІТКИЙ НЕЙРОН ДЛЯ ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ РОЗПІЗНАВАННЯ ОБРАЗІВ

This article explores a combined approach to training the double neo-fuzzy neuron, based on principles of supervised, unsupervised, and lazy learning. This approach aims to optimize the configuration of synaptic weights and the formation of membership functions in real-time mode. Importantly, the computational simplicity and minimal reliance on training data make this method versatile and applicable across various scenarios. It holds promise for developing a double neo-fuzzy system capable of effectively adapting to non-stationary data, even with limited training data.