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Back Propagation Based Firefly Algorithm for Character Recognition

Infanta Margret Frolic A, Bhuvana J

Abstract


Back Propagation (BP) in neural networks is a common technique used for character recognition which compute the output using feed forward network, calculate and back propagate the error signal through the network. The performance of back propagation falls off rapidly into local minima and suffers from slow convergences. To overcome this, back propagation is combined with other search algorithms such as genetic algorithm, firefly algorithm, particle swarm optimization, etc. The firefly algorithm which has advantage of high convergence rate and each firefly individually finds global optima in less number of iterations. In proposed work, firefly algorithm is combined with back propagation for character recognition.

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References


Li Fuliang, Gao Shuangxi, (2010) ’Character Recognition System Based on Back-propagation Neural Network’, International Conference on Ma-chine Vision and Human-machine Interface, China, 978-0-7695-4009-2/10.

M Abdul Rahiman, M S Rajasree, (2009) ’Printed Malayalam Character Recognition Using Back-propagation Neural Networks’, IEEE Interna-tional Advance Computing Conference, India, 978-1-4244-2928-8/09.

Lina Liu, Huijuan Qi, Jia Liu, (2012) ’The Research of Alphabet Identi-fication Based on Genetic BP Neural Network’, International Conference on Intelligent Human-Machine Systems and Cybernetics.

Gill.J, Singh.B, Singh.S, (2010) ’Training Back Propagation Neural Networks with Genetic Algorithm for Weather Forecasting’, Intelligent Systems and Informatics, 8th International Symposium.

Cao Bu-Qing, Liu Jian-xun, (2010) ’Currency Recognition Modelling Research Based on BP Neural Network Improved by Gene Algorithm’, Cambridge, MA: MIT Press, 2nd International Conference.

Jiao Aihong, Yuan Lizhe, (2012) ’City Fire Risk Assessment Model Based on the Adaptive Genetic Algorithm and BP Network’, International Conference on Industrial Control and Electronics Engineering.

Noraini Mohd Razali, John Geraghty (2011) ’Genetic Algorithm Perfor-mance with Different Selection Strategies in Solving TSP’, Proceedings of the World Congress on Engineering,U.K, Vol. 02.

Mohammad Hamdan, (2010) ’On The Disruption-Level Of Polynomial Mutation For Evolutionary Multi-Objective Optimization Algorithms’ , Jordan, pp.783800.

F. Herrera, M. Lozano, A. M. Sa nchez, (2003) ’A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study’, Spain, Vol. 18, pp. 309338.

Garima Singh, Laxmi Srivastava ’Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment’, Hindawi Publishing Corporation, Advances in Artificial Neural Systems Volume 2011, Article ID 532785.

Sudarshan Nandy, Partha Pratim Sarkar, Achintya Das, (2012) ’Analysis of a Nature Inspired Firefly Algorithm based Back-Propagation Neural Network Training’, International Journal of Computer Applications.

Luthra.J, Pal.S.K, (2011) ’A Hybrid Firefly Algorithm using Genetic Operators for the Cryptanalysis of a Monoalphabetic Substitution Cipher’, Information and Communication Technologies (WICT), World Congress.

Theofanis Apostolopoulo, Aristidis Vlachos, (2011) ’Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem’, Greece, doi:10.1155/2011/523806.




DOI: http://dx.doi.org/10.6084/ijact.v0i0.446

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