Towards Rough Neural Computing Based on Rough Membership Functions: Theory and Application

This paper introduces a neural network architecture based on rough sets and rough membership functions. The neurons of such networks instantiate approximate reasoning in assessing knowledge gleaned from input data. Each neuron constructs upper and lower approximations as an aid to classifying inputs. Rough neuron output has various forms. In this paper, rough neuron output results from the application of a rough membership function. A brief

• Type: Conference
• Year: 2000
• Where: RSCTC
• Authors: James F. Peters, Andrzej Skowron, Liting Han, Sheela Ramanna
• Keywords: Fuzzy Logic, Rough Membership Function, Rough Neuron Output, Rough Sets, RSCTC 2000