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RSCTC
2000
Springer

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

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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
James F. Peters, Andrzej Skowron, Liting Han, Shee
Added 25 Aug 2010
Updated 25 Aug 2010
Type Conference
Year 2000
Where RSCTC
Authors James F. Peters, Andrzej Skowron, Liting Han, Sheela Ramanna
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