Comparison of Learning Hierarchical Dynamic Neuro-neighborhood Models Based on Perceptron and Radial-basic Functions
Irina Sedykh, Vladimir Istomin · 2022 4th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) · 2022
This article will describe hierarchical dynamic neuro-neighborhood models. A review of previous studies in the field of hierarchical dynamic neuro-neighborhood modeling is carried out. The idea of hierarchical dynamic neuro-neighborhood models is the possibility of representing complex systems and their subprocesses using a neighborhood model with neural networks nested in its nodes. An algorithm for identifying a model of the «input-state-output» type is presented. The training of hierarchical dynamic neuro-neighborhood models based on a perceptron and a network of radial basis functions was compared.