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2009 WRI World Congress on Computer Science and Information Engineering
Implementation of Neural Network for Generalized Predictive Control: A Comparison between a Newton Raphson and Levenberg Marquardt Implementation
Los Angeles, California USA
March 31April 02
ISBN: 9780769535074
ASCII Text  x  
Sadhana K. Chidrawar, Sujata Bhaskarwar, Balasaheb M. Patre, "Implementation of Neural Network for Generalized Predictive Control: A Comparison between a Newton Raphson and Levenberg Marquardt Implementation," Computer Science and Information Engineering, World Congress on, vol. 1, pp. 669673, 2009 WRI World Congress on Computer Science and Information Engineering, 2009.  
BibTex  x  
@article{ 10.1109/CSIE.2009.849, author = {Sadhana K. Chidrawar and Sujata Bhaskarwar and Balasaheb M. Patre}, title = {Implementation of Neural Network for Generalized Predictive Control: A Comparison between a Newton Raphson and Levenberg Marquardt Implementation}, journal ={Computer Science and Information Engineering, World Congress on}, volume = {1}, year = {2009}, isbn = {9780769535074}, pages = {669673}, doi = {http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.849}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  CONF JO  Computer Science and Information Engineering, World Congress on TI  Implementation of Neural Network for Generalized Predictive Control: A Comparison between a Newton Raphson and Levenberg Marquardt Implementation SN  9780769535074 SP669 EP673 A1  Sadhana K. Chidrawar, A1  Sujata Bhaskarwar, A1  Balasaheb M. Patre, PY  2009 KW  Feedforward neural network KW  GPC KW  NGPC and Model predictive control VL  1 JA  Computer Science and Information Engineering, World Congress on ER   
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.849
An efficient implementation of Generalized Predictive Control using multilayer feed forward neural network as the plant’s nonlinear model is presented. Two algorithm i.e. Newton Raphson and Levenberg Marquardt algorithm are implemented and their results are compared. The details about this implementation are given. The utility of each algorithm is outlined in the conclusion. In using Levenberg Marquardt algorithm, the number of iteration needed for convergence is significantly reduced from other techniques. This paper presents a detail derivation of the neural generalized predictive control algorithm with Newton Raphson and Levenberg Marquardt as the minimization algorithm. A simulation result of Newton Raphson and Levenberg Marquardt algorithm are compared.Levenberg Marquardt algorithm shows a convergence of a good solution. The performance comparison of these two algorithms also given in terms of ISE and IAE.
Index Terms:
Feedforward neural network, GPC, NGPC and Model predictive control
Citation:
Sadhana K. Chidrawar, Sujata Bhaskarwar, Balasaheb M. Patre, "Implementation of Neural Network for Generalized Predictive Control: A Comparison between a Newton Raphson and Levenberg Marquardt Implementation," csie, vol. 1, pp.669673, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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