Knowledge extraction from artificial neural network models

Author

Boger, Zvi ; Guterman, Hugo

Author_Institution

Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel

Volume

4

fYear

1997

fDate

12-15 Oct 1997

Firstpage

3030

Abstract

The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, derivation of causal relationships between inputs and outputs, and analysis of the hidden neuron behavior in classification ANNs. An example of the application of these techniques is given of the faulty LED display benchmark. References of the application of these techniques are given of diverse large scale ANN models of industrial processes

Keywords

LED displays; identification; knowledge acquisition; neural nets; pattern classification; redundancy; artificial neural network models; causal relationships; classification; faulty LED display benchmark; hidden neuron identification; industrial processes; knowledge extraction; outputs; redundant input identification; Artificial neural networks; Data mining; Electronic mail; Industrial plants; Industrial relations; Industrial training; Large-scale systems; Neurons; Power system modeling; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1062-922X

Print_ISBN

0-7803-4053-1

Type

conf

DOI

10.1109/ICSMC.1997.633051

Filename

633051