On the realization of an optimal decision rule by means of a hierarchial system

This paper deals with the realization of a best approximation of an unknown optimal decision rule by means of a self-learning hierarchical system. This optimization implies the simultaneous use of adaptive estimation and classification techniques. An unsupervised learning discrete algorithm based on stochastic approximation theory is proposed as a solution to this problem. Some applications are suggested, particularly, in the field of modelling of static and dynamic non-linear relationships. Numerical results are given.