Fuzzy Pattern Recognition for Knowledge-Based Systems1

Knowledge-based systems have been severely restricted in areas where the speed of processing is a key factor. This is especially evident in large systems where the speed of knowledge-base searches is important. This paper proposes a fuzzy pattern recognition technique which identifies data patterns using possibility distributions and documents a fuzzy algorithm which is implemented. The technique is based on the theory of possibility. The results obtained using sensor data in manufacturing are encouraging: the fuzzy technique outperforms non-fuzzy techniques convincingly. The results for comparison with non-fuzzy techniques include shell-sort and quick-sort with binary search. The fuzzy technique identifies the correct pattern in the sensor database with nearly 99% accuracy. These results highlight the role of new fuzzy technologies for making knowledge-based systems more attractive in areas where they are currently limited by speed considerations.