Title :
Knowledge extraction from artificial neural network models
Author :
Boger, Zvi ; Guterman, Hugo
Author_Institution :
Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
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;
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-4053-1
DOI :
10.1109/ICSMC.1997.633051
