Fuzzy Rule Based Feature Extraction and Classification of Time Series Signal
Sandya H. B.1, Hemanth Kumar P.2, Himanshi Bhudiraja3, Susham K. Rao4
1Sandya H.B., PG Scholor, Department of ECE, AMC Engineering College, VTU, Bangalore, India.
2Hemanth Kumar P., Department. of ECE, AMC Engineering College, VTU, Bangalore, India.
3Himanshi Budhiraja., Department of ECE, AMC Engineering College, VTU, Bangalore, India.
4Susham K. Rao, Department of ECE, AMC Engineering College, VTU, Bangalore ,India.
Manuscript received on April 03, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 42-48 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1424053213/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Time series signal is a continuous signal which varies continuously with respect to time. These signals involve a great deal of useful information, the information content in these signals can be used for Feature Extraction and Classification. The purpose of Feature Extraction is to reduce the dimension of feature space and achieving better performances. The Features are extracted based on the mathematical calculations like Average, Maximum, Minimum, Standard Deviation and Variance. The Classification of extracted features is carried out by Fuzzy Rule based Selection System. Fuzzy Systems (FS) are evaluated for accuracy, multiplexity, flexibility and transparency for simple and complex systems. In this paper mamdani based Fuzzy System is used to achieve accurate results. Based on feature extracted data the Fuzzy System generates a fuzzy score and the Classifier Algorithm classify the feature extracted signals as Good, Bad and Best signals.
Keywords: Fuzzy, Feature Extraction, Classification, Time series signal.