A fault tolerant neuro-fuzzy inference system: using coarse-coded distributed representations

Authors:
Sriram G. Sanjeevi;Pushpak Bhattacharyya
Affiliations:
Dept. of Comp. Science & Engg., National Institute of Technology, Warangal, India;Dept. of Comp. Science & Engg., Indian Institute of Technology, Bombay, Mumbai, India
Venue:
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Year:
2005

Citing 5
Cited 0

Distributed representations

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Connectionist inference models

Neural Networks
Advances in SHRUTI—A Neurally Motivated Model of RelationalKnowledge Representation and Rapid Inference Using Temporal Synchrony

Applied Intelligence
A connectionist model for predicate logic reasoning using coarse-coded distributed representations

KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we describe a fault-tolerant Neuro-Fuzzy inference system for performing fuzzy reasoning using coarse-coded distributed representations. The system implements the fuzzy membership functions in a novel way using coarse-coded distributed representations for the inputs and outputs of neural networks. Distributed representations are known to give advantages of fault tolerance, generalization and graceful degradation of performance under noise conditions. Performance of the Neuro-Fuzzy inference system with regard to its ability to exhibit fault tolerance under noise conditions is studied. The system offered very good results of fault tolerance under noise conditions. It has also exhibited good generalization ability on unseen test inputs.