Evolution of fuzzy uncertainty in neural network learning

The paper explores the development of artificial neural network learning dynamics in terms of fuzzy uncertainty. In conventional artificial neural networks utilizing crisp variables, a set of error metrics required to achieve network convergence can be developed in the information-theoretic plane (based on the probabilistic uncertainty of the network variables). However, in a fuzzy neural network, consideration of fuzzy uncertainties can also facilitate a model to depict the convergence dynamics in the information-theoretic plane. A formulation is presented to achieve the fusion of a fuzzy neural network with the information-theoretic cost functions