Abstract
This paper investigates a novel technique for constructing and evaluating neuro-models of pattern generation. The modeling approach consists of two steps : first a neural network is trained to learn a core concept expressed on fuzzy logic, and then the trained network is augmented with additional processing nodes and connections.The augmented netwoek is then tested on its ability to solve problems related to the core concept for which its was trained. We present results from applying our model to monotone-function generation, decision making and image generation.
