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
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
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
Towards Rough Neural Computing Based on Rough Membership Functions: Theory and Application
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
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
Rough Neuron based on Pattern Space Partitioning
Neurocomputing
Towards an Ontology of Approximate Reason
Fundamenta Informaticae - Concurrency Specification and Programming Workshop (CS&P'2001)
Sensor, Filter, and Fusion Models with Rough Petri Nets
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P'2000)
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This paper introduces the design of rough neurons based on rough sets. Rough neurons instantiate approximate reasoning in assessing knowledge gleaned from input data. Each neuron constructs upper and lower approximations as an aid to classifying inputs. The particular form of rough neuron considered in this paper relies on what is known as a rough membership function in assessing the accuracy of a classification of input signals. The architecture of a rough neuron includes one or more input ports which filter inputs relative to selected bands of values and one or more output ports which produce measurements of the degree of overlap between an approximation set and a reference set of values in classifying neural stimuli. A class of Petri nets called rough Petri nets with guarded transitions is used to model a rough neuron. An application of rough neural computing is briefly considered in classifying the waveforms of power system faults. The contribution of this article is the presentation of a Petri net model which can be used to simulate and analyze rough neural computations.