Gradient Descent with Momentum Based Backpropagation Neural Network for Selection of Industrial Robot

Published on Jan 1, 2016
· DOI :10.1007/978-3-319-30933-0_49
Sasmita Nayak1
Estimated H-index: 1
(Government College),
B.B. Choudhury6
Estimated H-index: 6
(Indira Gandhi Institute of Technology),
Saroj Kumar Lenka13
Estimated H-index: 13
Fast development of industrial robots and its utilization by the manufacturing industries for many different applications is a critical task for the selection of robots. As a consequence, the selection process of the robot becomes very much complicated for the potential users because they have an extensive set of parameters of the available robots. In this paper, gradient descent momentum optimization algorithm is used with backpropagation neural network prediction technique for the selection of industrial robots. Through this proposed technique maximum, ten parameters are directly considered as an input for the selection process of robot where as up to seven robot parameter data be used in the existing methods. The rank of the preferred industrial robot evaluates from the perfectly the best probable robot that specifies the most genuine benchmark of robot selection for the particular application using the proposed algorithm. Moreover, the performance of the algorithms for the robot selection is analyzed using Mean Square Error (MSE), R-squared error (RSE), and Root Mean Square Error (RMSE).
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