Vibrated synchronization features neural network

Abstract

VSF-Network is a neural network model that learns dynamical patterns. It is hybrid neural network combining a chaotic neural network and a hierarchical neural network. The hierarchical neural network part is used for pattern learning. The chaotic neural network part monitors behavior of neurons in the middle layer of the hierarchical neural network. In this paper, two theoretical backgrounds of VSF-Network are introduced. An incremental learning moel using chaotic neural networks is introduced. The monitoring by chaotic neural network is based on the clusters of synchronized oscillators. Using the monitoring results, redundant neurons in the hierarchical neural network are found and they are used for learning of new patters. The second background is about the pattern recognition by combining learned patterns. The mechanism about recognition of combined learned patterns is explained by subspace selection in linear space. Through an experiment, its ability for the incremental learning and the pattern recognition are shown, and the factors influencing learning of VSF-Network are also shown.