NDLI: Using the NextClosure algorithm to extract rules from trained neural networks application in solar energy systems

Content Provider	IEEE Xplore Digital Library
Author	Vimieiro, R. Zarate, L.E. Pereira, E.M.D. Vieira, N.J.
Copyright Year	2005
Description	Author affiliation: Appl. Comput. Intelligence Lab., Pontifical Catholic Univ. of Minas Gerais, Brazil (Vimieiro, R.; Zarate, L.E.; Pereira, E.M.D.)
Abstract	Due to their capability of dealing with nonlinear problems, artificial neural networks (ANN) is widely used with several purposes. Once trained, they are capable to solve unprecedented situations, keeping tolerable errors in their outputs. However, humans cannot assimilate the knowledge kept by those nets, since such knowledge is implicitly represented by their connections weights. So, in order to facilitate the extraction of rules that describe the knowledge of ANN, formal concept analysis (FCA) and the NextClosure algorithm have been used. Such method is presented in this work, combining ANN, FCA and the NextClosure algorithm to compute the minimal implication base (Stem Base). As an example, solar energy systems are the domain application considered here, due to their importance as substitutes of traditional energy systems.
Starting Page	184
Ending Page	189
File Size	660293
Page Count	6
File Format	PDF
ISBN	0780389425
DOI	10.1109/SMCIA.2005.1466970
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-28
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Intelligent networks Solar energy Artificial neural networks Data mining Artificial intelligence Computational intelligence Laboratories Humans Algorithm design and analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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