Vibrate Synchronize Function neural network model — Its backgrounds
Yoshitsugu Kakemoto, Shinichi Nakasuka · 2014
VSF-Network, Vibrate Synchronize Function Network, is a hybrid neural network combining a Chaos Neural Network with a hierarchical network. VSF-Network is designed for symbol learning by a neural network. It finds unknown parts of input data by comparing to learned pattern and it learns unknown patterns using unused part of the network. The new patterns are learned incrementally and they are represented as sub-networks with unused parts of hierarchical neural network. Combinations of patterns are represented as combinations of the sub-networks. The combinations of symbols are represented as combinations of the sub-networks. In this paper, the two theoretical backgrounds of VSF-Network are introduced. At the first, an incremental learning framework with Chaos Neural Networks is introduced. Next, the pattern recognition with the combined with symbols is introduced. By Stochastic Catastrophe Model, the authors explain the combined pattern recognition. Through an experiment, both the incremental learning capability and the pattern recognition with pattern combination. Index Terms: Incremental learning, Chaos Neural network, Nonlinear dynamics, Stochastic Catastrophe Model.