A Combined Network Architecture Using Art2 and Back Propagation for Adaptive Estimation of Dynamic Processes

Abstract

A neural network architecture called ART2/BP is proposed. Thc goal has been to construct an artificial neural network that learns incrementally an unknown mapping, and is motivated by the instability found in back propagation (BP) networks: after first learning pattern A and then pattern B, a BP network often has completely 'forgotten' pattern A. A network using both supervised and unsupervised training is proposed, consisting of a combination of ART2 and BP. ART2 is used to build and focus a supervised backpropagation network consisting of many small subnetworks each specialized on a particular domain of the input space. 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