DocumentCode :
2409082
Title :
Weight value testing in artificial neural networks
Author :
Belfore, Lee A., II
Author_Institution :
Dept. of Electr. & Comput. Eng., Old Dominion Univ., Norfolk, VA, USA
Volume :
5
fYear :
1997
fDate :
12-15 Oct 1997
Firstpage :
4023
Abstract :
This paper presents a method for testing ANN interconnection weight values in programmable ANNs. The methodology requires separate weights for excitatory and inhibitory contributions to a neuron´s activation. In the test the excitatory and inhibitory weights are set to be equal, producing no net contribution to the activation. Any faults in the weight values result in an imbalance that is amplified by the neuron activation function and is easily detectable at the neuron output. Analysis is performed on the detection approach that includes fault detection thresholds as a function of neuron gain, network size, and weight perturbation. A methodology is outlined that uses the proposed testing approach followed by example test results
Keywords :
neural net architecture; neural nets; activation; artificial neural networks; excitatory; fault detection thresholds; inhibitory; interconnection weight values; neuron activation function; weight value testing; Artificial neural networks; Circuit faults; Circuit testing; Fault detection; Integrated circuit interconnections; Intelligent networks; Manufacturing; Neurons; Performance analysis; Performance gain;
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.1997.637287
Filename :
637287
Link To Document :
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