NDLI: Data Fusion Algorithm based on Functional Link Artificial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Jiawei Zhang Keqi Wang Qi Yue
Copyright Year	2006
Description	Author affiliation: Electromech. Eng. Coll., Northeast Forestry Univ., Harbin (Jiawei Zhang; Keqi Wang)
Abstract	Data fusion is the process of combining data from several sources into a single unified description of a situation. The sensor output value is estimated by some data fusion algorithms. In this paper, functional link artificial neural networks (FLANN) and data fusion technique are combined for removing the ambient temperature disturbance to enhance accuracy and reliability of lumber moisture content sensors (LMCS). Three different functional expansions, Chebyshev, Legendre and power series are studied. Simulation results show that the performance of Chebyshev polynomials is superior to the other two FLANN model. Compared with MLP, C-FLANN exhibits a much simpler structure, less training computation and faster convergence. So it is easier to implement by hardware and improve the performance-price ratio of system. The experimental results show that FLANN data fusion method can eliminate effectively the measurement errors and get reliable, real-time, accuracy estimated output of sensor
Starting Page	2806
Ending Page	2810
File Size	205272
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1712876
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Sensor fusion Temperature sensors Chebyshev approximation Moisture Computational modeling Polynomials Power system modeling Convergence Hardware lumber moisture content Data fusion functional link artificial neural networks measurement
Content Type	Text
Resource Type	Article

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