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Hierarchical neural networks for autonomous data analysis and decision makingA neural network based data analysis and decision making system to increase the autonomy of a planetary rover or similar exploratory vehicle is presented. A hierarchical series of neural networks for real time analysis of scientific images is used. The system under development emphasizes analysis of multispectral images by classifier and feature detector neural networks, to provide information on the mineral composition of a scene. A hierarchy of alternating analysis and decision making networks is being developed to allow increasingly fine scale analysis in regions of the image that are potentially important. It is noted that this system will facilitate both the selection of high priorty scientific information for transmission to earth, and the autonomous collection of rocks and soil for sample return.
Document ID
19900043180
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Eberlein, Susan
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Yates, Gigi
(JPL Pasadena, CA, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1988
Subject Category
Cybernetics
Meeting Information
Meeting: Annual Conference of the AAAIC
Location: Dayton, OH
Country: United States
Start Date: October 25, 1988
End Date: October 27, 1988
Sponsors: Texas Instruments, Dayton Special Interest Group for Artificial Intelligence, Systems Research Labs Inc.
Accession Number
90A30235
Distribution Limits
Public
Copyright
Other

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