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
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;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.682273