An accelerated particle swarm optimization based levenberg marquardt back propagation algorithm Nawi,NM, Khan,A, Rehman,MZ, Aziz,MA, Tutut Herawan and Jemal H. Abawajy 2014, An accelerated particle swarm optimization based levenberg marquardt back propagation algorithmNeural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part II, Springer Verlag, Heidelberg, Germany, pp.245-253, doi: 10.1007/978-3-319-12640-1_30. Attached Files Name Description MIMEType Size Downloads Title An accelerated particle swarm optimization based levenberg marquardt back propagation algorithm Author(s) Nawi,NM Khan,A Rehman,MZ Aziz,MA Tutut Herawan Jemal H. Abawajy orcid.org/0000-0001-8962-1222 Title of book Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part II Publication date 2014 Series Lecture notes in computer science ; v.8835 Chapter number 30 Total chapters 71 Start page 245 End page 253 Total pages 9 Publisher Springer Verlag Place of Publication Heidelberg, Germany Keyword(s) Artificial neural networks Levenberg marquardt back propagation Meta-heuristic optimization Nature inspired algorithms Particle swarm optimization Summary The Levenberg Marquardt (LM) algorithm is one of the most effective algorithms in speeding up the convergence rate of the Artificial Neural Networks (ANN) with Multilayer Perceptron (MLP) architectures. However, the LM algorithm suffers the problem of local minimum entrapment. Therefore, we introduce several improvements to the Levenberg Marquardt algorithm by training the ANNs with meta-heuristic nature inspired algorithm. This paper proposes a hybrid technique Accelerated Particle Swarm Optimization using Levenberg Marquardt (APSO_LM) to achieve faster convergence rate and to avoid local minima problem. These techniques are chosen since they provide faster training for solving pattern recognition problems using the numerical optimization technique.The performances of the proposed algorithm is evaluated using some bench mark of classification’s datasets. The results are compared with Artificial Bee Colony (ABC) Algorithm using Back Propagation Neural Network (BPNN) algorithm and other hybrid variants. Based on the experimental result, the proposed algorithms APSO_LM successfully demonstrated better performance as compared to other existing algorithms in terms of convergence speed and Mean Squared Error (MSE) by introducing the error and accuracy in network convergence. ISBN 9783319126395 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-12640-1_30 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 890101 Fixed Line Data Networks and Services HERDC Research category B1 Book chapter ERA Research output type B Book chapter Copyright notice ©2014, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30069186 Document type: Book Chapter Collections: Faculty of Science, Engineering and Built Environment School of Information Technology ERA Postprints 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 21 Jan 2015, 15:40:53 EST Wed, 21 Jan 2015, 15:41:23 EST Mon, 02 Feb 2015, 09:40:39 EST Mon, 02 Feb 2015, 09:42:04 EST Mon, 02 Feb 2015, 13:15:33 EST Fri, 10 Apr 2015, 11:21:54 EST Fri, 10 Apr 2015, 11:28:24 EST Sat, 18 Apr 2015, 06:11:39 EST Mon, 20 Apr 2015, 11:44:00 EST Mon, 20 Apr 2015, 11:48:05 EST Tue, 21 Apr 2015, 14:38:19 EST Tue, 19 May 2015, 14:14:25 EST Tue, 19 May 2015, 14:28:58 EST Tue, 26 May 2015, 10:57:38 EST Fri, 12 Jun 2015, 13:37:12 EST Mon, 23 Nov 2015, 09:41:20 EST Fri, 06 Apr 2018, 14:20:46 EST Sat, 16 Jun 2018, 02:30:12 EST Filtered Full Citation counts:   Cited 6 times in TR Web of Science Cited 18 times in Scopus Search Google Scholar Access Statistics: 692 Abstract Views, 23 File Downloads  -  Detailed Statistics Created: Mon, 02 Feb 2015, 09:40:39 EST

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