2D spiral pattern recognition with possibilistic measures

The main task for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane. This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications, i.e. the spiral coils with time. Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks. This paper describes a fuzzy approach which outperforms previous work in terms of the recognition rate and the speed of recognition. The paper presents the new approach and results with the validation and test sets. The results show that it is possible to solve the spiral problem in a relatively small amount of time with the fuzzy approach (up to 100% correct classification on the validation and test set; 77.2% correct classification with cross-validation using the leave-one-out method).