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

Judy Chen; Andrew A. Kostrzewski; Dai Hyun Kim; Gajendra D. Savant; Jeongdal Kim; Anatoly A. Vasiliev

doi:10.1117/12.284218

13 October 1997 Fuzzy controlled neural network for sensor fusion with adaptability to sensor failure

Judy Chen, Andrew A. Kostrzewski, Dai Hyun Kim, Gajendra D. Savant, Jeongdal Kim, Anatoly A. Vasiliev

Author Affiliations +

Proceedings Volume 3165, Applications of Soft Computing; (1997) https://doi.org/10.1117/12.284218
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

Citation Download Citation

Judy Chen, Andrew A. Kostrzewski, Dai Hyun Kim, Gajendra D. Savant, Jeongdal Kim, and Anatoly A. Vasiliev "Fuzzy controlled neural network for sensor fusion with adaptability to sensor failure", Proc. SPIE 3165, Applications of Soft Computing, (13 October 1997); https://doi.org/10.1117/12.284218

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