NDLI: The Lockheed probabilistic neural network processor

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[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering

IEEE Conference on Neural Networks for Ocean Engineering (Cat. No.91CH3064-3)

Neural-network performance assessment in sonar applications

All neural network sonar discrimination system

An episodal neural-net computing approach to the detection and interpretation of underwater acoustic transients

Acoustic transient analysis using wavelet decomposition

Adaptive kernel classifiers for short-duration oceanic signals

A neural network-based passive sonar detection and classification design with a low false alarm rate

Minehunting with multi-layer perceptrons

Constrained neural networks for recognition of passive sonar signals using shape

Sonar scene analysis using neurobionic sound segregation

Neural network for underwater target detection

Texture estimation with neural networks

Applications of neural networks to ocean acoustic tomography

Experimental comparison between neural networks and classical techniques of classification applied to natural underwater transients identification

Recent advances in pattern recognition and their potential application in active sonar classification

Modeling constant best delay-sensitive neurons and tracking neurons in the auditory cortex of the FM bat with a back-propagation neural network

A neural network for data association in a multiple-target tracking system

Mission adaptable autonomous vehicles

Design of an intelligent control system for remotely operated vehicles

Neural network control of a robotic manipulator arm for undersea applications

An integrated neurocomputing architecture for side-scan sonar target detection

Continuous inference networks for autonomous systems

Neural network learning rules for control: application to AUV tracking control

Underwater robotic operations using a decentralized adaptive neurocontroller

The potential of a neural network based sonar system in classifying fish

Neural network modeling of radar backscatter from an ocean surface using chaos theory

Classification of euceratium gran. in neural networks

Mapping of ocean sediments, by networks of parallel interpolating units

Model based classification of transient signals using the MLANS neural network

Neural-network-based classification of acoustic transients

ARACHYDE: a sensor-to-situation assessment software architecture for passive acoustic signal understanding

Evaluation of neural network and conventional techniques for sonar signal discrimination

Artificial neural networks for simultaneous and independent range and depth discrimination in passive acoustic localization

Classification of underwater acoustic transients by artificial neural networks

Neural networks and the classification of complex sonar signals

Fuzzy min-max classification with neural networks

An overview of the CMAC neural network

The Lockheed probabilistic neural network processor

Evolutionary methods for training neural networks

A new design methodology for optimal interpolative neural networks with application to the localization and classification of acoustic transients

ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network

Calibrating the performance of neural networks

A characterization of neural network performances based on Fokker-Planck statistical models

Neural networks for classification of ARMA models: an experimental study

Fundamental neural structures, operations, and asymptotic performance criteria in decentralized binary hypothesis testing

The Lockheed probabilistic neural network processor

Content Provider	IEEE Xplore Digital Library
Author	Washburne, T.P. Okamura, M.M. Specht, D.F. Fisher, W.A.
Copyright Year	1991
Abstract	The probabilistic neural network processor (PNNP) is a custom neural network parallel processor optimized for the high-speed execution (three billion connections per second) of the probabilistic neural network (PNN) paradigm. The performance goals for the hardware processor were established to provide a three order of magnitude increase in processing speed over existing neural net accelerator cards (HNC, FORD, SAIC). The PNN algorithm compares an input vector with a training vector previously stored in local memory. Each training vector belongs to one of 256 categories indicated by a descriptor table, which is previously filled by the user. The result of the comparison/conversion is accumulated in bins according to the original training vector's descriptor byte. The result is a vector of 256 floating-point works that is used in the final probability density function calculations.<>
Starting Page	309
Ending Page	315
File Size	606551
Page Count	7
File Format	PDF
ISBN	0780302052
DOI	10.1109/ICNN.1991.163367
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1991-08-15
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Neural network hardware Runtime environment Circuit simulation Neural networks Random access memory Backpropagation algorithms Read-write memory Pattern recognition Neurofeedback
Content Type	Text
Resource Type	Article

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