HUNG-CHANG LIAO2026-10-162026-10-162007-08-01Pure ID: 14905734Pure UUID: f3c6c376-1838-454f-b814-2acb8ede04ba10.1007/s00170-006-0551-1https://www.scopus.com/pages/publications/34547778930https://scholars.csmu.edu.tw/handle/123456789/22344During 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.10enjournal article