Robust parameter design for signal-response systems by soft computing

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.