DocumentCode
2208143
Title
An efficient modified probabilistic neural network hardware implementation for zero crossing thresholded binary signals
Author
Zaknich, Anthony
Author_Institution
Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia
Volume
1
fYear
1998
fDate
4-8 May 1998
Firstpage
256
Abstract
An efficient form of the modified probabilistic neural network is developed for the detection of Doppler shifted zero crossing thresholded binary chirp signals and other similar signals. The normal modified probabilistic neural network algorithm is based on a Gaussian radial basis function and the Euclidean distance measure requiring complex arithmetic operations. By using a simple tophat radial basis function and hamming distance measure in conjunction with binary signals it is possible to simplify the repetitive arithmetic operations to produce a more efficient form of the modified probabilistic neural network. This new form can produce more accurate correlator detector outputs than a multiple correlator detector system for moderate to high signal to noise ratios
Keywords
Doppler shift; correlation methods; feedforward neural nets; filtering theory; nonlinear filters; signal detection; Doppler shifted zero crossing thresholded binary chirp signals; Euclidean distance measure; Gaussian radial basis function; correlator detector outputs; hamming distance measure; modified probabilistic neural network hardware; tophat radial basis function; zero crossing thresholded binary signals; Arithmetic; Chirp; Correlators; Equations; Euclidean distance; Hamming distance; Neural network hardware; Neural networks; Signal processing algorithms; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7576
Print_ISBN
0-7803-4859-1
Type
conf
DOI
10.1109/IJCNN.1998.682273
Filename
682273
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