Results 191 to 200 of about 18,805 (220)

Ensembles of the Mamdani Fuzzy Systems

2012
This chapter describes a family of fuzzy systems that use neural network like approach for learning and visualizing the system.Models in this chapter have their antecedents and consequents of rules connected by a t-norm. Such systems are called the Mamdani type neuro-fuzzy systems and they are the most common neuro-fuzzy systems. As it is emphasized in
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Chaos Control Using Fuzzy Controllers (Mamdani Model)

2006
Controlling a strange attractor, or say, a chaotic attractor, is introduced in this chapter. Because of the importance to control the undesirable behavior in systems, researchers are investigating the use of linear and nonlinear controllers either to get rid of such oscillations (in power systems) or to match two chaotic systems (in secure ...
Ahmad Harb, Issam Al-Smadi
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NEW OPERATORS FOR CONTEXT ADAPTATION OF MAMDANI FUZZY SYSTEMS

Applied Artificial Intelligence, 2006
In this paper we introduce a set of tuning operators that allow us to implement context adaptation of fuzzy rule-based systems while keeping semantics and interpretability. The idea is to achieve context adaptation by starting from a (possibly generic) fuzzy system and adjusting one or more its components, such as membership function shape, fuzzy set ...
BOTTA A., LAZZERINI, BEATRICE, MARCELLONI, FRANCESCO   +2 more
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MAMDANI FUZZY SYSTEM FOR INDOOR AUTONOMOUS MOBILE ROBOT

AIP Conference Proceedings, 2011
Several control algorithms for autonomous mobile robot navigation have been proposed in the literature. Recently, the employment of non‐analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation.
M. K. A. Ahamed Khan, Razif Rashid, I. Elamvazuthi, Nader Barsoum, Jeffrey Frank Webb, Pandian Vasant   +5 more
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Learning flexible structured linguistic fuzzy rules for mamdani fuzzy systems

2009 IEEE International Conference on Fuzzy Systems, 2009
One significant challenge in building fuzzy systems for complex problems is the "curse of dimensionality". For the sake of a reduced size of the knowledge base, some rules with incomplete premise structures covering larger areas of the input domain are often desirable.
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Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing

International Journal of Information Management, 2021
Ilias O Pappas, Arch G Woodside
exaly  

Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research: A contemporary overview

Technological Forecasting and Social Change, 2022
Satish Kumar, Saumyaranjan Sahoo, Weng Marc Lim   +2 more
exaly  

Neuro-Fuzzy Architectures Based on the Mamdani Approach

2002
The fuzzy inference neural networks (see Section 3.3) that realize the inference based on the Mamdani approach are the subject of this chapter. Different, multi-layer, architectures of the neuro-fuzzy systems are portrayed. The systems with various fuzzifiers (singleton, non-singleton), defuzzifiers, and inference operations, are considered.
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The combined use of symmetric and asymmetric approaches: partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis

International Journal of Contemporary Hospitality Management, 2021
S Mostafa Rasoolimanesh, Christian M Ringle, Marko Sarstedt   +2 more
exaly  

A review of fuzzy AHP methods for decision-making with subjective judgements

Expert Systems With Applications, 2020
Yan Liu, Claudia M Eckert
exaly