Looking for a new AI paradigm: Evolving connectionist and fuzzy connectionist systems—Theory and applications for adaptive, on-line intelligent systems

Abstract

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. 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 classification, prediction, decision making and control: phoneme-based speech recognition; moving person identification; wastewater flow time-series prediction and control; intelligent agents; financial time series prediction and control. The principles of recurrent ECOS and reinforcement learning are outlined.