Electronic Resource
Boston, USA and Oxford, UK
:
Blackwell Publishers Inc
Computational intelligence
17 (2001), S. 0
ISSN:
1467-8640
Source:
Blackwell Publishing Journal Backfiles 1879-2005
Topics:
Computer Science
Notes:
This paper introduces an application of a particular form of rough neural computing in signal analysis. The form of rough neural network used in this study is based on rough sets, rough membership functions, and decision rules. Two forms of neurons are found in such a network: rough membership function neurons and decider neurons. Each rough membership function neuron constructs upper and lower approximation equivalence classes in response to input signals as an aid to classifying inputs. In this paper, the output of a rough membership function neuron results from the computation performed by a rough membership function in determining degree of overlap between an upper approximation set representing approximate knowledge about inputs and a set of measurements representing certain knowledge about a particular class of objects. Decider neurons implement granules derived from decision rules extracted from data sets using rough set theory. A decider neuron instantiates approximate reasoning in assessing rough membership function values gleaned from input data. An introduction to the basic concepts underlying rough membership neural networks is briefly given. An application of rough neural computing in classifying the power system faults is considered.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1111/0824-7935.00160
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