Patent ID: 6456989
Filing Date: 2002-09-24
Classification: G06N

Abstract:
A learning system for a pre-wired-rule-part neuro in a hierarchical network comprising said antecedent membership function realizing part, one or a plurality of said rule parts, and said consequent membership function realizing/non-fuzzy-processing part, whereinsaid rule part is not completely connected between adjacent layers of all units, but is partially connected internally, according to the control rules, between said antecedent membership function realizing part in the preceding step and said consequent membership function realizing/non-fuzzy-processing part in the following step, or between adjacent layers in said rule part; and outputs one or a plurality of control operation values (Y) corresponding to inputted control state values (X1, X2, - - - Xn); and the following three-step process is performed: the first step to initialize a weight value according to the knowledge pre-stored in said antecedent membership function realizing part or according to random numbers and to initialize a weight value according to the knowledge pre-stored in said rule part and consequent membership function realizing/non-fuzzy-processing part; the second step to learn a weight value of said antecedent membership function realizing part according to the learning data; and the third step to learn a total weight value of said antecedent membership function realizing part, said rule part, and said consequent membership function realizing/non-fuzzy-processing part according to the learning data.