Abstract
Construction of experimental design is one of the most important fields in statistics. Balanced incomplete block design(BIBD), one of the most useful designs, is available to save the number of experiments. However, in general, it is not easy to construct a BIBD since we need theory of finite field, finite projective geometry, Latin square and so on. Feed-Forward neural networks are applied in many statistical data analysis such as regression analysis, discriminant analysis and so on. However, Hopfield neural network which has feedback effects is not taken notice though it is shown to be useful in traveling salesman problem and other many optimal problems. In this paper, we consider a method to construct a BIBD. The method is based on the numerical computation of a dynamical system induced from a Hopfield neural network. It is shown that the method is successful by trial and error method with random initial points.
