A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

Krystian Łapa; Krzysztof Cpałka; A.I. Galushkin

doi:10.1007/978-3-319-19324-3_41

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A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

Krystian Łapa Krzysztof Cpałka A.I. Galushkin

Year: 2015 Lecture notes in computer science Pages: 448-468 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-319-19324-3_41

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Keywords:

Interpretability Computer science Neuro-fuzzy Artificial intelligence Fuzzy logic Notation Nonlinear system Fuzzy control system Semantics (computer science) Machine learning Mathematics Programming language Arithmetic

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Fuzzy Logic and Control Systems

Physical Sciences → Computer Science → Artificial Intelligence

Neural Networks and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Statistical and Computational Modeling

Physical Sciences → Computer Science → Artificial Intelligence

A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

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