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Incremental learning with balanced update on receptive fields for multi-sensor data fusion

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3 Author(s)
Jianbo Su ; Dept. of Autom., Shanghai Jiaotong Univ., China ; Jun Wang ; Xi, Yugeng

This paper addresses multi-sensor data fusion with incremental learning ability. A new cost function is proposed for the receptive field weighted regression (RFWR) algorithm based on the idea of back propagation (BP), so that the computation efficiency and the learning strategy of the modified RFWR are much more applicable for multi-sensor data fusion problem. Thus a new fusion structure and algorithm with incremental learning ability is constructed by adopting the modified RFWR algorithm together with the weighted average algorithm. Experiments of a two-camera unified positioning system are implemented successfully to test the proposed computation structure and algorithms.

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:34 ,  Issue: 1 )

Date of Publication:

Feb. 2004

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