Research on information requirement of first-order universal implication operators in fuzzy reasoning

Abstract

Based on the definition of linear specificity measure, this paper discusses detailedly the conditions on which the first-order universal implication operators satisfy the information boundedness principle in fuzzy reasoning, and gets the corresponding conclusion: when fuzzy propositions have positive measuring errors for their membership grades, first-order universal implication operators satisfy the information boundedness principle only if they are rejecting or restraining correlative; when they have negative ones, the operators satisfy the principle only if they are restraining correlative. This conclusion has important directive meaning for how to give the value of the general correlative coefficient h in practical control application. © 2005 by International Federation for Information Processing.