DocumentCode
1482208
Title
Worst case analysis of weight inaccuracy effects in multilayer perceptrons
Author
Anguita, Davide ; Ridella, Sandro ; Rovetta, Stefano
Author_Institution
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
Volume
10
Issue
2
fYear
1999
fDate
3/1/1999 12:00:00 AM
Firstpage
415
Lastpage
418
Abstract
We derive a method for the analysis of weight quantization effects in multilayer perceptrons based on the application of interval arithmetic. Differently from previous results, we find worst case bounds on the errors due to weight quantization, that are valid for every distribution of the input or weight values. Given a trained network, our method allows us to easily compute the minimum number of bits needed to encode its weights
Keywords
multilayer perceptrons; pattern classification; quantisation (signal); interval arithmetic; trained network; weight inaccuracy effects; weight quantization effects; worst case analysis; Algorithm design and analysis; Arithmetic; Computer aided software engineering; Computer networks; Multilayer perceptrons; Noise robustness; Nonhomogeneous media; Performance analysis; Quantization; Registers;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.750571
Filename
750571
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