A recursive neural system for memorizing systems of values arranged in a tree like structure

It is pointed out that, in general, adaptive automata have a cost function for organizing relations between input signals and output signals. But most of these automata have been studied with an a priori fixed cost function. For this reason the authors introduce a self-constructing value system with profits or losses. This ability helps the automaton to adapt to its environment. In the proposed method, the values system is founded on a priori fixed values (cost functions); then suitable elements are added to the values system by using the correlation between new elements and existing values. In the proposed model the values of the concepts will be modified during the experiences, and the values will control the learning process