NDLI: An intelligent model of LWA using distributed kernel

Content Provider	IEEE Xplore Digital Library
Author	Huaqiu Wang Xiaofeng Liao Changxiu Cao
Copyright Year	2008
Description	Author affiliation: Comput. Coll., ChongQing Inst. of Technol., Chongqing (Huaqiu Wang; Xiaofeng Liao; Changxiu Cao)
Abstract	This paper researches the possibility of using locally weighted algorithm for intelligent modeling of a nonlinear system for vanadium extraction in metallurgical process and proposes some optimized methods by finding the optimized regression coefficients by gradient descent and kernel function bandwidth by weighted distance. But kernel matrix computation for high dimensional data source demands heavy computing power. To overcome the computational difficulties of kernel functions and shorten the computing time, the paper designs a distributed algorithm to compute the kernel function matrix of LWA. The paper then implements the algorithm on a cluster of computing workstations using MPI. This paper studies the possibility of LWA using distributed kernel computing for predictive modeling for vanadium extraction in metallurgical process. Finally, the practical data are used to study the speedups and accuracy of the algorithm. The experimental results show that optimized locally weighted algorithm using distributed kernel outperforms the traditional RBF, RFWR and LWPR methods when significant amounts of noise are added, and the computing time has been shortened.
Starting Page	6491
Ending Page	6496
File Size	118596
Page Count	6
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4592883
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Bandwidth Distance measurement Training Clustering algorithms Algorithm design and analysis Computational modeling intelligent model locally weighted algorithm distributed kernel computing gradient descent weighted distance
Content Type	Text
Resource Type	Article

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