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 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
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.