A Neoteric Approach to Rough Neuro Fuzzy Methods

The paper presents a new hybridization methodology involving neural, fuzzy and rough computing. A rough sets based approximation technique has been proposed based on a certain neuro-fuzzy architecture. A new rough neuron composition consisting of a combination of a lower bound neuron and a boundary neuron has also been described. The conventional convergence of error in back propagation has been given away for a new framework based on ´output excitation factor´ and an inverse input transfer function. The paper also presents a brief comparison of performances, of the existing rough neural networks and ANFIS architecture against the proposed methodology. It can be observed that the rough approximation based neuro-fuzzy architecture is superior to its counterparts.