ART2/BP Architecture for Adaptive Estimation of Dynamic Processes

The goal has been to construct a supervised artificial neural network that learns incrementally an unknown mapping. As a result a network consisting of a combination of ART2 and backpropagation is proposed and is called an "ART2/BP" network. The ART2 network is used to build and focus a supervised backpropagation network. The ART2/BP network has the advantage of being able to dynamically expand itself in response to input patterns containing new information. Simulation results show that the ART2/BP network outperforms a classical maximum likelihood method for the estimation of a discrete dynamic and nonlinear transfer function. 1 INTRODUCTION Most current neural network architectures such as backpropagation require a cyclic presentation of the entire training set to converge. They are thus not very well suited for adaptive estimation tasks where the training vectors arrive one by one, and where the network may never see the same training vector twice. The ART2/BP network system is an attempt to construct a network that works well on these problems. Main features of our ART2/BP are: • implements incremental supervised learning • dynamically self-expanding