Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile properties of co...

In pattern recognition, statistical modeling, or regression, the amount of data is the most critical factor a ecting the performance. If the amount of data and computational resources are near in nite, many algorithms will provably converge to the optimal solution. When this is not the case, one has to introduce regularizers and a-priori knowledge to supplement the available data in order to bo...

The effects of diffusion anisotropy on pattern formation in bistable media are studied using a FitzHugh-Nagumo reaction-diffusion model. A relation between the normal velocity of a front and its curvature is derived and used to identify distinct spatiotemporal patterns induced by the diffusion anisotropy. In a wide parameter range anisotropy is found to have an ordering effect: initial patterns...

A significant problem associated with application of the Back Propagation learning paradigm for pattern classification is the lack of high accuracy in generalization when the domain is large. In this paper we describe a multiple neural network system, which uses two self-organizing neural networks that work as teaching data filters (feature extractors), producing information that is used to tra...