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Proceedings Paper

Neural Network Signal Processor (NSP)
Author(s): Patrick F Castelaz; Dwight E Mills
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Paper Abstract

Recent applied and theoretical results have demonstrated that certain classes of neural networks possess adaptive characteristics. These adaptive networks modify their response to inputs as a result of "experience," e.g., they are trained. One of the most obvious applications of this type of trainable network is to real-time signal processing problems. In particular, for many applications, the detection and classification of specific "target" signatures buried in noisy, clutter-rich signals often proves to be an extremely difficult problem. In addition, this problem is nearly always worsened by the high bandwidth associated with many modern sensor systems. Nearly all conventional signal processing and neural network techniques employ special-purpose feature extraction hardware as an interface between the sensor and the detection/classification mechanism (whether neural net or conventional). However, given typical system requirements for minimum size, weight and power, a considerable advantage would be gained if this interface were simplified or eliminated. Trainable neural networks appear to offer exceptional promise as simultaneous feature extraction and pattern recognition mechanisms. This paper presents the results of preliminary experimental investigations of the performance of various trainable (back-propagation) neural networks applied to the processing of various types of sensor signals.

Paper Details

Date Published: 20 April 1988
PDF: 8 pages
Proc. SPIE 0880, High Speed Computing, (20 April 1988); doi: 10.1117/12.944038
Show Author Affiliations
Patrick F Castelaz, Hughes Aircraft Company (United States)
Dwight E Mills, Hughes Aircraft Company (United States)


Published in SPIE Proceedings Vol. 0880:
High Speed Computing
David P. Casasent, Editor(s)

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