Evolving Connectionist Systems Evolving Connectionist and Fuzzy-Connectionist Systems for On-line Adaptive Decision Making and Control

The paper contains a discussion material and preliminary experimental results on a new approach to building on-line, adaptive decision making and control systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on-line learning. They can accommodate any new input data, including new features, new classes, etc. New connections and new neurons are created during operation. The ECOS framework is illustrated here 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 fuzzy neural networks, trained with the backpropagation algorithm or with a genetic algorithm. ECOS are appropriate techniques to use for creating on-line, real-time, adaptive intelligent systems. This is illustrated on a case study problem of on-line, wastewater time-series flow prediction and cont rol. Possible real world applications of this approach are discussed.