In spite of their successes in various systems, researchers have pointed out certain drawbacks [4,5] in the mechanism, which motivates the introduction of Similarity-based Approximate Reasoning (SAR) mechanism as proposed in [4-7].
Combining the conventional CRI and the existing SAR methods, in this paper we extend the works of [4,5] to develop a novel approach to approximate reasoning.
Section 2 includes a brief introduction of some basic notions and state of the art on approximate reasoning techniques.
So far there have been several approaches to approximate reasoning based on fuzzy set or interval-valued fuzzy set, in which the most influential methods are CRI and SAR algorithms.
Although the CRI algorithm had achieved notable success in various fields such as fuzzy control, expert system and decision-making support, some defects of this method were found in terms of inference mechanism, which leads to the yield of another important approach of approximate reasoning--Similarity-based Approximate Reasoning (SAR).
In , a similarity-based approximate reasoning method was given based on IVFSs and fitness techniques.
Here, we intend to integrate the above techniques for an adequate theory of similarity-based approximate reasoning.