Updating a priori information in fuzzy pattern recognition to improve classification performance

The main aim of this paper is to develop and implement the concept of incremental learning for fuzzy statistical classifiers. Such a scheme involves continuous modification of training data as learning progresses and is implemented with classifier systems that adapt to incremental information. This paper discusses the implementation of the above approach using a real-time fuzzy classifier system. The recognition performance of this approach on the three spiral benchmark is compared with conventional static training. The paper also discusses the generalisation performance of the system in the context of recognising noisy spiral data.