NDLI: Agent based adaptive firefly back-propagation neural network training method for dynamic systems

Content Provider	IEEE Xplore Digital Library
Author	Nandy, S. Karmakar, M. Sarkar, P.P. Das, A. Abraham, A. Paul, D.
Copyright Year	2012
Description	Author affiliation: Machine Intelligence Research Labs (MIR Labs), Auburn, Washington 98071-2259, USA (Sarkar, P.P.) \|\| Department of Computer Science and Engineering, JIS college of Engineering, Kalyani, Nadia, West Bengal, India (Das, A.) \|\| Department of Engineering & Technological Studies, University of kalyani, Nadia, West Bengal, India (Karmakar, M.) \|\| Tech Mahindra Limited, Plot-1, Phase-Ill., Rajiv Gandhi Infotech Park, Pune-411057, India (Paul, D.) \|\| Department of Engineering & Technological Studies, University of Kalyani, Nadia, West Bengal, India (Nandy, S.) \|\| Department of Eletronics & Tele. Coram. Engineering, Kalyani Govt. Engg. College, Nadia, West Bengal, India (Abraham, A.)
Abstract	Nature Inspired meta-heuristic algorithms are one of the most efficient solution to many engineering optimization problems. The Firefly algorithm is one of the nature inspired solution. The objective of the proposed work is of two folds. In the first fold the firefly algorithm is applied to the back-propagation training phase to optimize the overall training process. One of the problem in this type of implementation is the adjustment of algorithmic parameters and number of firefly population, and for a dynamic system the manual modification of parameter is a troublesome matter. In the second fold, the proposed work is implemented a statistical hypothesis based agent which is adaptively control the various parameters and number of firefly populations in firefly algorithm based back-propagation method and this makes it more convenient for dynamic systems. The effectiveness of automatic parameter adjustment over the performance of algorithm is analyzed through correct classification rate and sum of squared error. The proposed method is tested over five bench mark non-linear standard data set and it is compared with genetic algorithm based back-propagation method. It is observed from the experiment that the agent automatically adjust the parameters and number of firefly populations in each iteration of the back-propagation optimization phase and it is finally converged within a minimum number of iteration.
Starting Page	449
Ending Page	454
File Size	1127554
Page Count	6
File Format	PDF
ISBN	9781467351140
e-ISBN	9781467351164
DOI	10.1109/HIS.2012.6421376
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	meta-heuristic algorithm statistical hypothesis Firefly algorithm Back-propagation training optimization Hybrid intelligent systems
Content Type	Text
Resource Type	Article

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