Abstract
The paper presents some new methods of knowledge acquisition and processing with regard to neuro-fuzzy systems. Various connectionist architectures that reflect fuzzy IF-THEN rules are considered. The so-called flexible neuro-fuzzy systems are described, as well as relational systems and probabilistic neural networks. Other connectionist systems, such hierarchical neuro-fuzzy systems, type 2 systems, and hybrid rough-neuro-fuzzy systems are mentioned. Finally, the perception-based approach, which refers to computing with words and perceptions, is briefly outlined. Within this framework, a multi-stage classification algorithm and a multi-expert classifier are proposed.
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Rutkowska, D. (2004). Knowledge Acquisition and Processing: New Methods for Neuro-Fuzzy Systems. In: Van Emde Boas, P., Pokorný, J., Bieliková, M., Štuller, J. (eds) SOFSEM 2004: Theory and Practice of Computer Science. SOFSEM 2004. Lecture Notes in Computer Science, vol 2932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24618-3_7
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DOI: https://doi.org/10.1007/978-3-540-24618-3_7
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