A neural network approach to crystal structure classification

Shetty, K. R. and Rao, A. and Gopala, K. (1999) A neural network approach to crystal structure classification. CURRENT SCIENCE, 76 (5). pp. 670-676.

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This paper focuses classification of crystal classes in a periodic table using the known neural network (NN) learning algorithm, viz, generalized delta rule (GDR) by feeding the set of input features in max-min-max sub arrays. We have taken eighteen independent physical parameters for each element, trained the network from atomic number (AN) 1 to 84 and we validated the crystal class from AN 86 to 95 from the trained network and achieved 100 per cent accuracy, which was later extended from AN 96 to 120. Further, we have also evaluated the dependencies of the neural network in different confidence intervals and hidden layers, We would like to call this learning algorithm as max-min-max GDR.

Item Type: Article
Subjects: Physical Sciences > Physics
Divisions: PG Campuses > Manasagangotri, Mysore > Physics
Depositing User: Users 13 not found.
Date Deposited: 19 Apr 2013 13:27
Last Modified: 04 Sep 2013 05:03
URI: http://eprints.uni-mysore.ac.in/id/eprint/4957

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