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Abstract
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Article Information:
The Research on Curling Track Empty Value Fill Algorithm Based on Similar Forecast
Zhao Peiyu and Li Shangbin
Corresponding Author: Zhao Peiyu
Submitted: October 31, 2012
Accepted: January 03, 2013
Published: July 10, 2013 |
Abstract:
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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.
Key words: Artificial Neural Network (ANN), Autonomous Hybrid Power System (AHPS), curling track, Static Var Compensator (SVC), , ,
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Cite this Reference:
Zhao Peiyu and Li Shangbin, . The Research on Curling Track Empty Value Fill Algorithm Based on Similar Forecast. Research Journal of Applied Sciences, Engineering and Technology, (08): 1472-1478.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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