Title :
Limiting the effects of weight errors in feedforward networks using interval arithmetic
Author :
Anguita, Davide ; Ridella, Sandro ; Rovetta, Stefano ; Zunino, Rodolfo
Author_Institution :
Genoa Univ., Italy
Abstract :
We address in this work the problem of weight inaccuracies in digital and analog feedforward networks. Both kind of implementations suffer from this problem due to physical limits of the particular technology. This work presents a novel and effective approach through the application of interval arithmetic to the multilayer perceptron. Results show that our method allows one to (1) compute strict bounds of the output error of the network, (2) find robust solutions respect to weight inaccuracies and (3) compute the minimum weight precision required to obtain the desired performance of the network
Keywords :
feedforward neural nets; multilayer perceptrons; feedforward networks; interval arithmetic; multilayer perceptron; output error; robust solutions; weight errors; weight inaccuracies; Circuit noise; Computer networks; Digital arithmetic; Electronic mail; Equations; Feeds; Intelligent networks; Noise reduction; Robustness; Usability;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.548928
