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IEEE Transactions on Cybernetics, 2013
In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting ...
Farnood, Merrikh-Bayat +1 more
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In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting ...
Farnood, Merrikh-Bayat +1 more
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2017
Die in Kap. 8 behandelte Neuro-Fuzzy-Technologie vereint die Vorteile der Fuzzy-Logik mit ihren Moglichkeiten, unscharfe Mengen mathematisch zu behandeln, mit denen kunstlicher neuronaler Netze, deren Wissen in den Eigenschaften und der Vernetzung der einzelnen Neuronen gespeichert ist.
Zbigniew A. Styczynski +2 more
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Die in Kap. 8 behandelte Neuro-Fuzzy-Technologie vereint die Vorteile der Fuzzy-Logik mit ihren Moglichkeiten, unscharfe Mengen mathematisch zu behandeln, mit denen kunstlicher neuronaler Netze, deren Wissen in den Eigenschaften und der Vernetzung der einzelnen Neuronen gespeichert ist.
Zbigniew A. Styczynski +2 more
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Proceedings of 19th Convention of Electrical and Electronics Engineers in Israel, 2002
The concept of fuzzy logic has been incorporated into the neural network so as to enable a system to deal with cognitive uncertainties in a manner more like humans. This integration yields the neuro fuzzy system, that captures the benefits of the fuzzy logic as well as the neural network tools into a single approach. Many neuro fuzzy-based applications
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The concept of fuzzy logic has been incorporated into the neural network so as to enable a system to deal with cognitive uncertainties in a manner more like humans. This integration yields the neuro fuzzy system, that captures the benefits of the fuzzy logic as well as the neural network tools into a single approach. Many neuro fuzzy-based applications
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2000
There are generally three approaches to building mathematical models: white box modeling, where everything is considered to be known from physical laws, black box modeling (system identification), where all knowledge derives from measurements, gray box modeling, where both physical laws and observed measurements are used to design a ...
Ernest Czogała, Jacek Łęski
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There are generally three approaches to building mathematical models: white box modeling, where everything is considered to be known from physical laws, black box modeling (system identification), where all knowledge derives from measurements, gray box modeling, where both physical laws and observed measurements are used to design a ...
Ernest Czogała, Jacek Łęski
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2001
This Chapter deals with neuro-fuzzy systems, i. e., those soft computing methods that combine in various ways neural networks and fuzzy concepts. Each methodology has its particular strengths and weaknesses that make it more or less suitable in a given context.
Andrea Tettamanzi, Marco Tomassini
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This Chapter deals with neuro-fuzzy systems, i. e., those soft computing methods that combine in various ways neural networks and fuzzy concepts. Each methodology has its particular strengths and weaknesses that make it more or less suitable in a given context.
Andrea Tettamanzi, Marco Tomassini
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1997 Annual Meeting of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.97TH8297), 2002
In this paper we introduce a new architecture called recurrent neuro-fuzzy (RNF) system which enhances the modeling capabilities of fuzzy systems with the dynamic behavior of recurrent neural networks (RNN). In a general sense, the architecture of RNF is similar to other adaptive neuro-fuzzy systems. It has a rule-base, a database, an inference engine,
C. Isik, M. Farrokhi
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In this paper we introduce a new architecture called recurrent neuro-fuzzy (RNF) system which enhances the modeling capabilities of fuzzy systems with the dynamic behavior of recurrent neural networks (RNN). In a general sense, the architecture of RNF is similar to other adaptive neuro-fuzzy systems. It has a rule-base, a database, an inference engine,
C. Isik, M. Farrokhi
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2018
A hybrid intelligent system involves combining two intelligent technologies; e.g., a combination of a neural network with a fuzzy system to produce a hybrid neuro-fuzzy system. Generally combining probabilistic reasoning, fuzzy logic, evolutionary computation together with neural networks produces hybrid systems which form the core of soft computing.
Alireza Hajian, Peter Styles
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A hybrid intelligent system involves combining two intelligent technologies; e.g., a combination of a neural network with a fuzzy system to produce a hybrid neuro-fuzzy system. Generally combining probabilistic reasoning, fuzzy logic, evolutionary computation together with neural networks produces hybrid systems which form the core of soft computing.
Alireza Hajian, Peter Styles
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FuBiNFS – fuzzy biclustering neuro-fuzzy system
Fuzzy Sets and Systems, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Rough set-based neuro-fuzzy system
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006Neuro-fuzzy hybridization is the oldest and most popular methodology in soft computing (Mitra & Hayashi, 2000). Neuro-fuzzy hybridization is known as Fuzzy Neural Networks, or Neuro-Fuzzy Systems (NFS) in the literature (Lin & Lee, 1996; Mitra & Hayashi, 2000).
null Kai Keng Ang, null Chai Quek
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