NDLI: Comparative study on EKF training algorithm with EM and MLE for ANN modeling of nonlinear systems

Content Provider	IEEE Xplore Digital Library
Author	Rajesh, M.V. Archana, R. Unnikrishnan, A. Gopikakaumari, R.
Copyright Year	2010
Description	Author affiliation: Federal Institute of Science & Technology, Mookkannur, Angamali, Kerala, India (Archana, R.) \|\| Govt. Model Engineering College, Thrikkakara (PO), Cochin, Kerala, India-682 021 (Rajesh, M.V.) \|\| Naval Physical & Oceanographic Laboratory, (DRDO-Govt. of India), Cochin, Kerala, India-682021 (Unnikrishnan, A.) \|\| School of Engineering, Cochin University of Science & Technology, CUSAT (PO), Kerala, India-682022 (Gopikakaumari, R.)
Abstract	The system identification/modeling problem looks for a suitably parameterized model, representing a given process. The parameters of the model are adjusted to optimize a performance function based on error between the given process output and identified process output. The linear system identification field is well established with many classical approaches whereas most of those methods cannot be applied for nonlinear systems. The problem becomes tougher if the system is completely unknown with only the output time series is available. It has been reported that the capability of Artificial Neural Network to approximate all linear and nonlinear input-output maps makes it predominantly suitable for the blind identification of nonlinear systems, where only the output time series is available. [1][2][4][5].The work reported here is an attempt for modeling certain nonlinear systems using recurrent neural networks, in which the network parameters are estimated using the Extended Kalman Filtering (EKF) and an extension of the same with EKF with Expectation Maximization (EM) (to alleviate the problems encountered in Kalman filtering). The paper also compares the performance of the neural network model implemented with Maximum Likelihood Estimation (MLE), An assessment on the model performances in terms of the mean square error (MSE) and computational complexity has also been done for these algorithms.
Starting Page	1407
Ending Page	1413
File Size	203419
Page Count	7
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Training Maximum likelihood estimation EKF Recurrent neural networks RNN Artificial neural networks EM Mathematical model Kalman filters State Space Modeling Nonlinear systems MSE
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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