抄録
Radius Basis Function Network (RBFN) can make up response surface of interpolation quite well even if it has multi-peak. Thus, we can optimize functions that is not explicitly expressed, such as we use in Engineering Analysis. In unconstraint case, it works successively to reduce a number of function calls, to obtain global optimum value and also to obtain over all response surface. On the other hand, when it is constrained, we need a number of function calls onry to find feasible region. In this study, we use RBF to classify feasible region by given data, and give a new data according to the information that is given by RBF classifier.
