Paper Info
Title
Design of Rough Neurons: Rough Set Foundation and Petri Net Model
Abstract
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.
Year
DOI
Venue
2000
10.1007/3-540-39963-1_30
ISMIS
Keywords
Field
DocType
input data,neuron construct,rough membership function,rough set,rough petri net,rough neural computing,filter input,rough neural computation,rough set foundation,rough neurons,classifying input,rough neuron,membership function,power system,petri net
Set theory,Petri net,Computer science,Waveform,Algorithm,Electric power system,Rough set,Artificial intelligence,Artificial neural network,Membership function,Machine learning,Computation
Conference
ISBN
Citations 
PageRank 
3-540-41094-5
8
0.92
References 
Authors
4
5
Name
Order
Citations
PageRank
James F. Peters11825184.11
Andrzej Skowron21170192.26
Zbigniew Suraj350159.96
L. Han4151.73
S. Ramanna59218.42