Research Article | OPEN ACCESS
The Research on Curling Track Empty Value Fill Algorithm Based on Similar Forecast
Zhao Peiyu and Li Shangbin
Harbin Engineering University, Harbin, China
Research Journal of Applied Sciences, Engineering and Technology 2013 8:1472-1478
Received: October 31, 2012 | Accepted: January 03, 2013 | Published: July 10, 2013
Abstract
The sparsity problem could result in a data-dependent reduction and we couldn’t do rough set null value estimates, therefore, we need to deal with the problem of a sparse data set before performing the null value estimate and padded by introducing a collaborative filtering technology used the sparse data processing methods - project- based score prediction in the study. The method in the case of the object attribute data sparse, two objects can be based on their known attributes of computing the similarity between them, so a target object can be predicted based on the similarity between the size of the other objects to the N objects determine a neighbor collection of objects and then treat the predicted target unknown property by neighbors object contains attribute values to predict.
Keywords:
Artificial Neural Network (ANN), Autonomous Hybrid Power System (AHPS), curling track, Static Var Compensator (SVC),
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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