Title :
Alternative neural network training methods [active sonar processing]
Author :
Porto, V.W. ; Fogel, David
Author_Institution :
Orincon Corp., San Diego, CA, USA
fDate :
6/1/1995 12:00:00 AM
Abstract :
Investigates three potential neural network training algorithms in processing active sonar returns. Although all three methods generate reasonable probabilities of detection and false alarm in discriminating between man-made objects and background events, the stochastic training methods of simulated annealing and evolutionary programming outperform backpropagation
Keywords :
backpropagation; genetic algorithms; learning (artificial intelligence); neural nets; probability; simulated annealing; sonar signal processing; stochastic processes; active sonar return processing; background events; backpropagation; detection probability; discriminating; evolutionary programming; false alarm probability; man-made objects; neural network training algorithms; simulated annealing; stochastic training methods; Genetic programming; Intelligent networks; Multilayer perceptrons; Neural networks; Object detection; Response surface methodology; Signal processing algorithms; Simulated annealing; Sonar; Stochastic processes;
Journal_Title :
IEEE Expert
