NDLI: RBF Neural Network based Model as an Optimal Classifier for the Classification of Radar Returns from the Ionosphere

Content Provider	IEEE Xplore Digital Library
Author	Salankar, S.S. Patre, B.M.
Copyright Year	2006
Description	Author affiliation: B. D. Coll. of Eng., Sevagram (Salankar, S.S.)
Abstract	Research into the problem of classification of radar returns from the ionosphere has been taken up as a challenging task for the neural networks (NNs). It appears from the literature review that for the Multi layer Perceptron (MLP) NN trained with backpropagation, reported average classification accuracy was about 96% on the test instances. This paper investigates and designs an optimal classifier using a radial basis function (RBF) NN. Authors compare the performance of two NN configurations, namely a well-known MLP NN model and the proposed RBF NN model on the radar dataset collected from the published studies. It is shown that the proposed RBF NN, consistently, has 100% accuracy on "bad" instances and 99.1935% accuracy on "good" instances. The results show that the proposed RBF NN classifier clearly outperforms the MLP NN one in various performance measures such as MSE, NMSE, correlation coefficients, area under the ROC curve and classification accuracy on the testing datasets even after attempting different data partitions.
Starting Page	2043
Ending Page	2048
File Size	6316602
Page Count	6
File Format	PDF
ISBN	1424407257
DOI	10.1109/ICIT.2006.372564
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-15
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Ionosphere Databases Testing Backpropagation algorithms Radar measurements Radar antennas Phased arrays Multilayer perceptrons Educational institutions Receiver Operating Characteristics Classification Multi-layer Perceptron Neural Network backpropagation algorithm Radial Basis Function Neural Network
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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