Title :
Random parameter variation in analog VLSI neural networks for linear image filtering
Author :
Shi, B.E. ; Roska, T. ; Chua, L.O.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
This paper introduces an analytic method to determine the sensitivity to random parameter variations of analog VLSI neural network architectures for linear image filtering. The authors compare the robustness of several different circuit architectures for low pass filtering. This method can also determine which components within a particular architecture should specified the most precisely
Keywords :
VLSI; analogue processing circuits; filtering theory; image processing; low-pass filters; neural chips; neural net architecture; analog VLSI neural networks; circuit architectures; linear image filtering; low pass filtering; random parameter variation; robustness; sensitivity; Cellular neural networks; Circuits; Computer architecture; Filtering; Intelligent networks; Low pass filters; Neural networks; Nonlinear filters; Robustness; Very large scale integration;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374453
