1995 年 10 巻 5 号 p. 752-760
This paper describes a method to infer the initial values of design variables for specifications satisfaction problems in design. The problems are given with specification variables, intermediate variables, design variables, and equality and inequality constraints among the variables. If some of specifications and ranges of all design variables are given, it is necessary to infer appropriate initial vaiues of design variables and the rest of specifications to solve and satisfy constraints. In this case, if a good case near to the givens is known, the initial values can adopt the case. If problems, however, demand a new design or constraints are unfamiliar, it is difficult to infer the initial values. To solve this difficulty, a method to infer the initial values for the given specifications is needed. In the proposed method, constraints are represented in Constraint Logic Programming (CLP) and the initial values are repeatedly inferred from the given specifications by a neural network (NN) which learns the relations among values of design and specification variables by some values pairs of them calculated with CLP. The inference continues changing the worst one of the pairs with a better pair calculated by CLP using inferred values gotten with NN untill the initial values converge. This method is applied to a design of laser graphic device and the validity of the method is confirmed.
