Bridging the fuzzy, neural and evolutionary paradigms for automatic target recognition

Applied Intelligence

Volumn 35, Issue 2, 2011, Pages 211-225

  Valova, Iren ^a   Milano, Gary ^b   Bowen, Kevin ^c   Gueorguieva, Natacha ^d  

^a UNIVERSITY OF MASSACHUSETTS DARTMOUTH   (United States)

^b RAYTHEON COMPANY   (United States)

^c Raytheon Integrated Defense Systems   (United States)

^d CITY UNIVERSITY OF NEW YORK   (United States)

Author keywords

Automatic target recognition; Feedforward neural networks; Fuzzy logic; Genetic algorithm

Indexed keywords

COMPUTATIONAL COSTS; CONFIGURATION CONTROL; FASTER CONVERGENCE; FEED-FORWARD; FUZZY FRONT END; HIGH RATE; HYBRID ARCHITECTURES; LASER DETECTION; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; NEURAL NET; NEUROCOMPUTING; OBJECT DETECTION; OPERATING ENVIRONMENT; OPTIMAL SETS; RANGE DATA; RECOGNITION PROPERTIES; TRAINING ALGORITHMS; TRAINING DATA; WEIGHT SET;

AUTOMATIC TARGET RECOGNITION; FEEDFORWARD NEURAL NETWORKS; FUZZY LOGIC; FUZZY NEURAL NETWORKS; MULTILAYER NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS;

GENETIC ALGORITHMS;

EID: 80052963584     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-010-0213-8     Document Type: Article

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