Patent ID: 6330553
Filing Date: 2001-12-11
Classification: G05B,Y10S

Abstract:
A control method for optimizing control of a machine by using a control unit including at least one control model of the machine, wherein(i) the model outputs a signal to control the machine when receiving input signals wherein the input-output relationship is regulated by at least one control parameter;(ii) the model includes a fuzzy neural network which has layers and receives as input at least two variables each having membership functions located in the layers, and which outputs said control parameter, wherein the input-output relationship is constituted using fuzzy rules formed by a combination of the membership functions, said control method comprising the steps of:(a) obtaining appropriate numbers of fuzzy rules in the fuzzy neural network by an autonomic method comprising:(I) training the fuzzy neural network to learn a relationship between input and output of the fuzzy neural network based on an error in output determined from teaching data, by changing coupling coefficients between adjacent layers, wherein the membership functions and the fuzzy rules are modified;(II) judging whether a change in an error in output or in coupling coefficients is within a predetermined range;(III) if the change is not within the predetermined range, adding to the fuzzy neural network a membership function related to at least one of the at least two variables, thereby adding fuzzy rules to the fuzzy neural network;(IV) judging whether any fuzzy rules are interpolated between the other fuzzy rules or extrapolated from the other fuzzy rules, wherein interpolation or extrapolation of a fuzzy rule corresponding to a membership function is determined based on linearity of coupling coefficients of at least two other membership functions with respect to one of the at least two variables; and(V) if linearity of coupling coefficients is established in a fuzzy rule, deleting the interpolated or extrapolated fuzzy rule; and(b) controlling the machine by using the at least one model with the updated fuzzy neural network.