Abstract—This paper has been presented the adaptive estimation for missing environmental parameters for short duration. The Radial Basis Function based Artificial Neural Network technique has been discussed and used this technique the estimation of the missing environmental parameters. This work assumes that data are missing completely at random. This implies that we expect the missing values or input vector to be deducible in some complex manner from the remaining data. Two cases of missing parameters have been considered, in first case one parameter is missing, and in second case two parameters are missing.
Index Terms—RBFNN, artificial neural network, missing data, random data.
The authors are with the Electrical and Computers Engineering Department, University of Seoul, South Korea (e-mail: anujkumariitd@gmail.com, drhskim@uos.ac.kr).
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Cite: Anuj Kumar and Hiesik Kim, "Adaptive Estimation of Missing Environmental Parameters Based on Radial Basis Function Neural Networks,"
International Journal of Computer Theory and Engineering vol. 5, no. 2, pp. 238-241, 2013.
