Use of intervals for soft classification in fuzzy neural networks

Neural networks can be used to classify input data into one of a given set of categories. With limited training sets, crisp neural network results are predictably poor. Incorporation of fuzzy techniques improves performance in these cases. Even though fuzzy neural networks classify imprecise data quite well, the incorporation of a soft decision classification lowers the error rate substantially. This paper discusses methods for soft decision making, including a method that uses intervals. A neuro-fuzzy system that classifies input vectors is examined. This neuro-fuzzy system not only uses intervals in a fuzzy neural network, but also employs a method of utilizing intervals in a soft decision for classification. This neuro-fuzzy system's performance in computer simulations is examined and compared with crisp neural networks' performance.