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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/12075
Title: Optimization of neural network parameters using Grey-Taguchi methodology for manufacturing process applications
Authors: Kumar D.
Gupta A.K.
Chandna P.
Pal M.
Published in: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Abstract: Performance of neural networks depends upon several input parameters. Several attempts have been made for optimization of neural network parameters using Taguchi methodology for achieving single objective such as computation effort, computation time, etc. Determination of optimum setting to these parameters still remains a difficult task. Trial-and-error method is one of the frequently used approaches to determine the optimal choice of these parameters. Keeping in view the problems with trial-and-error method, a systematic approach is required to find the optimum value of different parameters of neural network. In the present work, three most important distinct performance measures such as mean square error between actual and prediction, number of iteration, and total training time consumption have been probably considered first time concurrently. The multiobjective problem has been solved using Grey-Taguchi methodology. In this study, optimal combinations of different neural network parameters have been identified by using the Taguchi-based Grey relational analysis. The data set includes 81 sets of milling data corresponding to three-level full factorial experimental design for four cutting parameters, i.e. cutting speed, feed, axial depth of cut, and radial depth of cut, respectively. The output is average surface roughness for the experiment. The performance of different neural network models has been tabulated in L36 orthogonal array. Confidence interval has also been estimated for 95% consistency level to validate the optimum level of different parameters. It was found that the Taguchi-based Grey relational analysis approach can effectively be used as a structured method to optimize the neural network parameters settings, which can be easily implemented to enhance the performance of the neural network model with a relatively small size and time saving experiment. The result clearly indicates that the optimal combination of neural network parameters obtained by using the proposed approach performs better in terms of low mean square error, small number of iterations, and lesser training time required to perform the analysis which further results in lesser computation effort and processing time. Methodology proposed in this work can be utilized for any type of neural network application to find the optimum levels of different parameters. © IMechE 2014.
Citation: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (2015), 229(14): 2651-2664
URI: https://doi.org/10.1177/0954406214560598
http://repository.iitr.ac.in/handle/123456789/12075
Issue Date: 2015
Publisher: SAGE Publications Ltd
Keywords: Analysis of variance
Grey relational analysis
Manufacturing
Neural networks
Optimization
Taguchi method
ISSN: 9544062
Author Scopus IDs: 57202478211
55628526755
17433502400
7101848782
Author Affiliations: Kumar, D., Department of Mechanical Engineering, JCDM College of Engineering, Sirsa, India
Gupta, A.K., Department of Mechanical Engineering, Vaish College of Engineering, Rohtak, India
Chandna, P., Department of Mechanical Engineering, National Institute of Technology, Kurukshetra, India
Pal, M., Department of Civil Engineering, National Institute of Technology, Kurukshetra, India
Corresponding Author: Kumar, D.; Department of Mechanical Engineering, JCDM College of EngineeringIndia
Appears in Collections:Journal Publications [ME]

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