See title K. Murugesan, D. Paul Dhayabaran, E. C. Henry Amirtharaj, David J. Evans: A Fourth Order Embedded Runge-Kutta RKACeM(4,4) Method Based on Arithmetic and Centroidal Means with Error Control. Int. J. Comput. Math. 79(2): 247-269 (2002) A Fourth Order Embedded Runge-Kutta RKACeM(4,4) Method Based on Arithmetic and Centroidal Means with Error Control Authors: K. Murugesan a; D. Paul Dhayabaran a; E. C. Henry Amirtharaj a; David J. Evans a Affiliation: a Department of Mathematics and Computer Applications, Regional Engineering College, Tiruchirappalli-620015, Tamil Nadu, India. DOI: 10.1080/00207160211925 Publication Frequency: 12 issues per year Published in: International Journal of Computer Mathematics, Volume 79, Issue 2 2002 , pages 247 - 269 Subjects: Computing & Information Technology: Algorithms & Complexity; Computation: Algorithms & Complexity; Artificial Intelligence; Bioinformatics; Combinatorics; Computer & Software Engineering; Computer Engineering; Computer Science (General); Discrete Mathematics; Finite Mathematics; IT Security; Information Theory; Information & Communication Technology (ICT): Internet & Multimedia; Computing & Information Technology: Internet & Multimedia; Linear Programming; Logic; Mathematical Logic; Mathematical Modelling; Number Theory; Computation: Numerical Analysis; Analysis: Numerical Analysis; Statistics & Probability: Operations Research; Production Engineering: Operations Research; Manufacturing Engineering: Operations Research; Programming & Programming Languages; Security; Set Theory; Simulation & Modeling; Systems & Computer Architecture; Number of References: 18 Formats available: PDF (English) Purchase Article: US$32.00 - buy now add to cart [ show other buying options ] purchase type customer type online access payment method price Single Article Purchase Personal 3 days credit card US$32.00 buy now add to cart Issue Purchase Any permanent credit card US$427.17 buy now add to cart If you would like to pay in any other currency please see the purchasing help pages for more information. Sign In Online Sample Abstract In this paper, we introduce a new approach for solving IVPs with error control by formulating an embedded method involving RK methods based on Arithmetic Mean (AM) and Centroidal Mean (CeM). Numerical experiments reveal that the results obtained from this method agree well with the exact solution and also in match with the well known methods like Runge-Kutta Fehlberg (4,5), Runge-Kutta Merson and RK(4,4) introduced by Yaakub and Evans. Keywords: Fourth Order Runge-Kutta Methods; Arithmetic Mean; Centroidal Mean; Rk(4,4); Runge-Kutta Fehlberg Method; Runge-Kutta Merson Method view references (18) : view citations Publications of Author V. Murgesh, K. Murugesan Comparison of Numerical Integration Algorithms in Raster CNN Simulation. [Citation Graph (0, 0)][DBLP] AACC, 2004, pp:115-122 [Conf] J. Y. Park, David J. Evans, K. Murugesan, S. Sekar, V. Murugesh Optimal control of singular systems using the rk-butcher algorithm. [Citation Graph (0, 0)][DBLP] Int. J. Comput. Math., 2004, v:81, n:2, pp:239-249 [Journal] K. Murugesan, D. Paul Dhayabaran, E. C. Henry Amirtharaj, David J. Evans A Fourth Order Embedded Runge-Kutta RKACeM(4, 4) Method Based on Arithmetic and Centroidal Means with Error Control. [Citation Graph (0, 0)][DBLP] Int. J. Comput. Math., 2002, v:79, n:2, pp:247-269 [Journal] K. Murugesan, D. Paul Dhayabaran, E. C. Henry Amirtharaj, David J. Evans Numerical Strategy for the System of Second Order IVPs Using RK Method Based on Centroidal Mean. [Citation Graph (0, 0)][DBLP] Int. J. Comput. Math., 2003, v:80, n:2, pp:233-241 [Journal] K. Murugesan, S. Sekar, V. Murugesh, J. Y. Park Numerical solution of an industrial robot arm control problem using the RK-Butcher algorithm. [Citation Graph (, )][DBLP] IJCAT, 2004, v:19, n:2, pp:132-138 [Journal] ----- V. Murugesh and N. Regarajan An Efficient Numerical Integration Technique for Multi-Layer Raste CNN simulator Informatin technology Journal 6(2)pp 202-206 Abstract ... This smimulator is capable of performing Multi-Layer raster Simulation for any size of input image, thus a powerful tool for researchers investigating potential applications of CNN. This study reports an efficient algorithm exploiting the latency properties of Cellular Neural Networks along with popular numerical integration techniques; simulation results and comparisn are also presentd. Murugesh et al. wrote they used in their paper publisched in 2006 the RK-Butcher algorithm for time multiplexing scheme of CNN and Oliveria 1999 introdueced the popular RK-Gil algorithm ... @inproceedings{DBLP:conf/aacc/MurgeshM04, author = {V. Murugesh and K. Murugesan}, title = {Comparison of Numerical Integration Algorithms in Raster CNN Simulation.}, booktitle = {AACC}, year = {2004}, pages = {115-122}, ee = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3285{\&}spage=115}, crossref = {DBLP:conf/aacc/2004}, bibsource = {DBLP, http://dblp.uni-trier.de} } @proceedings{DBLP:conf/aacc/2004, editor = {Suresh Manandhar and Jim Austin and Uday B. Desai and Yoshio Oyanagi and Asoke K. Talukder}, title = {Applied Computing, Second Asian Applied Computing Conference, AACC 2004, Kathmandu, Nepal, October 29-31, 2004. Proceedings}, booktitle = {AACC}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = {3285}, year = {2004}, isbn = {3-540-23659-7}, bibsource = {DBLP, http://dblp.uni-trier.de} } V. Murugesh, K. Murugesan: Comparison of Numerical Integration Algorithms in Raster CNN Simulation. 115-122 Electronic Edition (link) BibTeX while in the abstract of this submitted paper we find "The function of the simulator is that it is capable of performing Raster Simulation for any kind as well as any size of input image. It is a powerful tool for researchers to examine the potential applications of CNN. This article proposes an efficient pseudo code for exploiting the latency properties of Cellular Neural Networks along with well known RK-Fourth Order Embedded numerical integration algorithms. Simulation results and comparison have also been presented" Single-layer CNN simulator Lee, C.-C.; de Gyvez, J.P. Circuits and Systems, 1994. ISCAS apos;94., 1994 IEEE International Symposium on Volume 6, Issue , 30 May-2 Jun 1994 Page(s):217 - 220 vol.6 Digital Object Identifier 10.1109/ISCAS.1994.409566 Summary:An efficient behavioral simulator for Cellular Neural Networks (CNN) is presented in this paper. The simulator is capable of performing Single-Layer CNN simulations for any size of input image, thus a powerful tool for researchers investigating potential applications of CNN. This paper reports an efficient algorithm exploiting the latency properties of Cellular Neural Networks along with numerical integration techniques; simulation results and comparisons are also presented Single-layer CNN simulator Lee, C.-C.; de Gyvez, J.P. Circuits and Systems, 1994. ISCAS apos;94., 1994 IEEE International Symposium on Volume 6, Issue , 30 May-2 Jun 1994 Page(s):217 - 220 vol.6 Time-multiplexing CNN simulator Chi-Chien Lee; Pineda de Gyvez, J. Circuits and Systems, 1994. ISCAS apos;94., 1994 IEEE International Symposium on Volume 6, Issue , 30 May-2 Jun 1994 Page(s):407 - 410 vol.6 2803: ON THE OPTIMAL CHOICE OF INTEGRATION TIME-STEP FOR RASTER SIMULATION OF A CNN FOR GRAY LEVEL IMAGE PROCESSING