Comparison of optimization by response surface methodology with neurofuzzy methods

We compare two approaches where empirical models are used to augment computer simulations to facilitate rapid device optimization. We apply the response surface model (RSM) methodology and neurofuzzy techniques to the problem of modeling simulations of the average flux density in the air gap of a loudspeaker. Both these techniques have significant advantages over more traditional methods of optimizing computer simulation experiments. We show that these techniques have different advantages and disadvantages depending on the problem being modeled. In particular, the use of domain knowledge is shown to give robust and reliably predictive RSM's. Neurofuzzy techniques are shown to be particularly suited to problems where little is known about the problem.