Performance evaluation of Baldwins fuzzy reasoning for large knowledge bases

The paper compares performance of Baldwin's fuzzy reasoning based on a fuzzy truth value with the fastest available solutions. The analysis is important in order to locate areas where improvement of the first is the most significant. Potential fast approach based on the fuzzy truth value would be very interesting for many users applying fuzzy systems to solve problems involved with complex knowledge bases. Particularly, all research considering an analysis of genes employing DNA microarrays. Such methods very often generate rules with thousands of atomic premises. The most valuable advantage of Baldwin's reasoning is preserving a fuzzy relation between a fact and a premise in the inference process, where other solutions, especially those commonly used, usually reduce it to only one value. Obtaining the method which, from computation time point of view, is comparable with common approaches but offers more advanced process of fuzzy reasoning, would be a significant achievement. The goal of this analysis is to prepare the future research considering development of Baldwin's method, which computational complexity is comparable to simple, fast and widely used solutions like systems based on the approach of Mamdani and Assilan or Larsen.