Advances in the Dempster-Shafer theory of evidence
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Reasoning about Data - A Rough Set Perspective
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Wavelets, Rough Sets and Artificial Neural Networks in EEG Analysis
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Classifying Faults in High Voltage Power Systems: A Rough-Fuzzy Neural Computational Approach
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Information Granules in Distributed Environment
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Design of Rough Neurons: Rough Set Foundation and Petri Net Model
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Neural Networks Design: Rough Set Approach to Continuous Data
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
A radar target multi-feature fusion classifier based on rough neural network
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Extracting classification rules with support rough neural networks
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Towards an Ontology of Approximate Reason
Fundamenta Informaticae - Concurrency Specification and Programming Workshop (CS&P'2001)
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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. I n this paper, rough neuron output results from the application of a rough membership function. A brief introduction to the basic concepts underlying rough membership neural networks i s g iven. A n application of rough neural computing is briefly considered in classifying the waveforms of power system faults. Experimental results with rough neural classification of waveforms are also given.