Dynamic Pattern Recognition for Temporal Data

The main aim of this paper is to demonstrate the performance of a possibility based classifier which implements dynamic pattern recognition. The objectives of the paper are: 1) to detail the working of a Massively Parallel Fuzzy System (MPFS) which can be used to classify two spiral data; 2) to develop and detail the dynamic pattern recognition approach; and 3) to discuss the results obtained. The results suggest that dynamic pattern recognition will perform high quality decision making for tasks involving uncertain, noisy and small amounts of data. The two spiral task can be solved with dynamic pattern recognition with more than 90% recognition success and this performance remains stable with increasingly noisy test sets.