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