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Proceedings Paper

Fuzzy controlled neural network for sensor fusion with adaptability to sensor failure
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Paper Abstract

Artificial neural networks have proven to be powerful tools for sensor fusion, but they are not adaptable to sensor failure in a sensor suite. Physical Optics Corporation (POC) presents a new sensor fusion algorithm, applying fuzzy logic to give a neural network real-time adaptability to compensate for faulty sensors. Identifying data that originates from malfunctioning sensors, and excluding it from sensor fusion, allows the fuzzy neural network to achieve better results. A fuzzy logic-based functionality evaluator detects malfunctioning sensors in real time. A separate neural network is trained for each potential sensor failure situation. Since the number of possible sensor failure situations is large, the large number of neural networks is then fuzzified into a small number of fuzzy neural networks. Experimental results show the feasibility of the proposed approach -- the system correctly recognized airplane models in a computer simulation.

Paper Details

Date Published: 13 October 1997
PDF: 9 pages
Proc. SPIE 3165, Applications of Soft Computing, (13 October 1997); doi: 10.1117/12.284218
Show Author Affiliations
Judy Chen, Physical Optics Corp. (United States)
Andrew A. Kostrzewski, Physical Optics Corp. (United States)
Dai Hyun Kim, Physical Optics Corp. (United States)
Gajendra D. Savant, Physical Optics Corp. (United States)
Jeongdal Kim, Physical Optics Corp. (United States)
Anatoly A. Vasiliev, Physical Optics Corp. (United States)


Published in SPIE Proceedings Vol. 3165:
Applications of Soft Computing
Bruno Bosacchi; James C. Bezdek; David B. Fogel, Editor(s)

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