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Efficient Optimization of Neural Network using Taguchi-grey Relational Analysis with Signal-to-Noise Ratio Approach for 2.5D End Milling Process
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Volume 5, 2018
Issue 2 (June)
Pages: 30-42   |   Vol. 5, No. 2, June 2018   |   Follow on         
Paper in PDF Downloads: 60   Since Jul. 24, 2018 Views: 1112   Since Jul. 24, 2018
Dinesh Kumar, Department of Mechanical Engineering, JCDM College of Engineering, Sirsa, India.
Pankaj Chandna, Department of Mechanical Engineering, National Institute of Technology, Kurukshetra, India.
Mahesh Pal, Department of Civil Engineering, National Institute of Technology, Kurukshetra, India.
Artificial neural network (ANN) is amongst one of the most popular nonlinear mapping systems in the field of artificial intelligence which has the ability to take care of numerous problems including modelling, predicting, and measuring in experimental knowledge. The execution of ANN relies on few data parameters. Deciding the optimum setting of different parameters till date remains a troublesome assignment. An efficient methodology is needed to obtain the optimum values for various parameters of ANN. In the present study, Grey-Taguchi’s method is used to determine the optimum value of various input parameters of the ANN model trained by different algorithms for multi-objective problem. Optimum values of different ANN parameters have been found by utilizing the Grey-Taguchi analysis in combination with analysis of variance (ANOVA) and S/N ratio analysis. The outcome of conformational experiments clearly demonstrates that the optimum mix of ANN parameters acquired by utilizing the proposed methodology performs better as far as low value of mean square error (MSE) and a low number of iterations needed to perform the investigation are in consideration, which advances results in the lesser calculation effort and time for execution. The methodology suggested in this work can be used for various neural network applications to locate the optimum levels of various parameters.
Optimization, Grey-Taguchi, Neural Networks, Analysis of Variance, End Milling, S/N Ratio
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