Evolving Fuzzy Neural Networks: Theory and Applications for On-line Adaptive Prediction, Decision Making and Control

The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on-line learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. New connections and new neurons are created during the operation of the system. The ECOS framework is presented and illustrated on a particular type of evolving neural networks evolving fuzzy neural networks. ECOS are three to six orders of magnitude faster than the multilayer perceptrons, or the fuzzy neural networks, trained with the backpropagation algorithm, or with a genetic programming technique. ECOS belong to the new generation of adaptive intelligent systems. This is illustrated on several real world problems for adaptive, on-line prediction, decision making and control.