Title :
Neural network for image Fourier transform classification
Author :
Levchenko, E.B. ; Myl´nikov, G.D. ; Timashev, A.N. ; Turygin, A. Yu
Author_Institution :
TRINITI, Moscow Region, Russia
Abstract :
Considers the performance of a neural-network (NN)-based visual control system with NNs of different types (multilayered perceptrons and Hamming nets). They discuss the possible compensation of disturbances arising in a coherent-optical processor by NN learning. Simulation shows that different NNs have different behaviors for two types of input distorted data: the perceptron NN is more suitable for compensation of optical tract errors while the winner-takes-all NN performs better for noise damaged input patterns
Keywords :
Fourier transform optics; computer vision; neural nets; Hamming nets; coherent-optical processor; disturbance compensation; image Fourier transform classification; input distorted data; learning; multilayered perceptrons; neural-network; noise damaged input patterns; optical tract errors; simulation; visual control system; winner takes all network; Fourier transforms; Image processing; Image recognition; Neural networks; Nonlinear optical devices; Nonlinear optics; Optical computing; Optical devices; Optical fiber networks; Optoelectronic devices;
Conference_Titel :
Neuroinformatics and Neurocomputers, 1992., RNNS/IEEE Symposium on
Conference_Location :
Rostov-on-Don
Print_ISBN :
0-7803-0809-3
DOI :
10.1109/RNNS.1992.268593
