Titlebook: Neuro-Fuzzy Architectures and Hybrid Learning; Danuta Rutkowska Book 2002 Springer-Verlag Berlin Heidelberg 2002 Artificial Intelligence.F

的事物

Neuro-Fuzzy Architectures Based on the Logical Approach,n approximation, control, and classification problems, are portrayed in Section 5.5. In order to train the systems, gradient, genetic, or hybrid al-gorithms can be applied. The learning methods are described in Chapter 6. In particular, the architecture-based learning, outlined in Section 6.1.3, is recommended.

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農(nóng)學(xué)

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Description of Fuzzy Inference Systems,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 s

搖晃

Neural Networks and Neuro-Fuzzy Systems,[87], and then in [167], [166], [491], [273], [169], [157], [228], [503], [270], and others. . have been applied in many consumer products [492], [493]. They incorporate some merits of both . and .. In the . combinations we distinguish . (see Section 3.2), obtained by introducing fuzziness directly

construct

Neuro-Fuzzy Architectures Based on the Mamdani Approach,ent, multi-layer, architectures of the neuro-fuzzy systems are portrayed. The systems with various fuzzifiers (singleton, non-singleton), defuzzifiers, and inference operations, are considered. All these systems can be trained, when applied to solve practical problems, similarly to neural networks.

Chagrin

Neuro-Fuzzy Architectures Based on the Logical Approach,ly,in Section 5.1, the mathematical descriptions of the neuro-fuzzy systemsemploying different fuzzy implications are determined. Then, the connec-tionist, multi-layer, architectures, which correspond to the implicationbased systems, are presented. These architectures are proposed in [366].The neuro

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