An adaptive data sorter based on probabilistic neural networks

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 the 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 is described.<>