Abstract
During the past decade, the methodology of parameter design for signal-response systems has received increasing attention in engineering applications. Several approaches, based on statistical techniques, are presented to resolve the problems. These approaches intend to make the signal-response relationship insensitive to the noise variation by choosing a combination of control factors. In this work, we propose an alternative methodology consisting of three phases based on soft computing to optimize signal-response systems. The first phase involves training a backpropagation neural network to construct the response model of a signal-response system. The response model is then used to predict the responses of a specific experimental run. The second phase develops performance measures for three types of signal-response systems to evaluate the predicted responses. The third phase involves transforming performance measures into energy values and presenting the optimization process, which minimizes the energy value by performing a simulated annealing algorithm. The proposed methodology is illustrated with a numerical example.
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Chang, HH., Hsu, CM. & Liao, HC. Robust parameter design for signal-response systems by soft computing. Int J Adv Manuf Technol 33, 1077–1086 (2007). https://doi.org/10.1007/s00170-006-0551-1
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DOI: https://doi.org/10.1007/s00170-006-0551-1