Abstract
Approximate reasoning, based on fuzzy sets and fuzzy logic, has been successfully employed in fuzzy inference systems. These systems are used in many practical applications, mainly as fuzzy controllers, but also as other knowledge-based systems such as expert systems, fuzzy classifiers and so on. Fuzzy systems have been recently combined with neural networks and genetic algorithms to create different kinds of neuro-fuzzy systems and intelligent systems. This chapter presents an overview of fuzzy sets, approximate reasoning, and fuzzy systems.
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© 2002 Springer-Verlag Berlin Heidelberg
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Rutkowska, D. (2002). Description of Fuzzy Inference Systems. In: Neuro-Fuzzy Architectures and Hybrid Learning. Studies in Fuzziness and Soft Computing, vol 85. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1802-4_2
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DOI: https://doi.org/10.1007/978-3-7908-1802-4_2
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2500-8
Online ISBN: 978-3-7908-1802-4
eBook Packages: Springer Book Archive