Evolving Fuzzy Neural Networks for Adaptive, On-line Intelligent Agents and Systems
نویسنده
چکیده
This paper discusses and illustrates one paradigm of neuro-fuzzy techniques for building on-line, adaptive intelligent agents and 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 orders of magnitude faster than multilayer perceptrons, or fuzzy neural networks and they belong to the new generation of adaptive intelligent systems. ECOS are suitable techniques for building intelligent agents on the WWW, intelligent mobile robots and embedded systems. An ECOS based structure of an intelligent agent is proposed and discussed.
منابع مشابه
Chapter 7. Evolving Connectionist and Fuzzy - Connectionist Systems: Theory and Applications for Adaptive, On-line Intelligent Systems
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, online learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. New connections and new neurons are created ...
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