DocumentCode :
2629668
Title :
An adaptive data sorter based on probabilistic neural networks
Author :
Wang, C. David ; Thompson, James P.
Author_Institution :
AIL Systems Inc., Melville, NY, USA
fYear :
1991
fDate :
18-21 Nov 1991
Firstpage :
1296
Abstract :
Based on a self-organized, probabilistic neural network (PNN) paradigm, a parallel network can be used to sort data parameters into classes with high sorting accuracy and low fragmentation. The capabilities of the sorter, as applied to ESM (electronic support measure) pulse-data sorting, are shown. The PNN implements the statistical Bayesian strategy by computing a joint probability density over all input data parameters to match a group of candidate data classes. The sorting is accomplished by assigning then inputs to the most likely group with highest probability density estimate. Based on test data from an ESM system, the PNN has shown significant improvement over conventional rule-based techniques. The parallel computer architecture of PNN is well-suited for VLSI chip implementation. An 80000 gate semicustom chip design concept is described
Keywords :
Bayes methods; neural nets; parallel architectures; probability; self-adjusting systems; sorting; ESM pulse data sorting; VLSI chip; adaptive data sorter; data matching; electronic support measure; joint probability density; parallel computer architecture; parallel network; probabilistic neural networks; self organised neural nets; statistical Bayesian strategy; Bayesian methods; Knowledge based systems; Measurement standards; Neural networks; Neurons; Probability density function; Pulse measurements; Sorting; Statistics; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
Type :
conf
DOI :
10.1109/IJCNN.1991.170576
Filename :
170576
Link To Document :
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