Massively parallel fuzzy systems: The case of three spiral pattern recognition

The main objectives of this paper are: 1) to describe the working of a Massively Parallel Fuzzy System; 2) to test the system on a new benchmark, the three spiral data set; and 3) to describe the behaviour of the system when solving the problem. The described system is aimed at solving pattern recognition problems in real-time. Pattern recognition data are subjected to non-iterative decision making through the estimation of class membership of test data. This paper describes the performance of this system on the temporal three spiral benchmark. The task is to learn three class data which lies on three distinct spirals that coil around each other and around the origin with time. There are no linear solutions to this problem (33% recognition rate with discriminant analysis). The system under consideration classifies the training set with 100% success and recognises training data with up to 89% (without the use of rejection threshold) and 98% success (with a rejection threshold of theta =.8). The behavioural aspects of the system are also studied by quantifying the recognition rate as a function of spiral radius and rejection threshold.