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Some remarks on adaptive neuro-fuzzy systems
Proceedings of Tenth International Symposium on Intelligent Control, 1996In this brief note we make three remarks concerning adaptive implementations of neural networks and fuzzy systems. First, we bring to the reader's attention the fact that the potential power of these systems as function approximators is lost when, as in some recently published works, the adjustable parameters are only the linear combination weights of ...
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Flexible weighted neuro-fuzzy systems
Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02., 2003In the paper we study new neuro-fuzzy systems. They are called the OR-type fuzzy inference systems (NFIS). Based on the input-output data we learn not only parameters of membership functions but also a type of the systems and aggregating parameters. We propose the weighted T-norm and S-norm to neuro-fuzzy inference systems. Our approach introduces more
L. Rutkowski, K. Cpalka
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Fuzzy-Systeme und Neuro-Fuzzy-Systeme
1998Die Anwendung von Fuzzy-Systemen hat sich in den letzten Jahren immer mehr in Bereiche der Betriebswirtschaft, Datenanalyse, Medizin usw. erstreckt und ist keineswegs mehr auf technische Anwendungen beschrankt. Gleichzeitig spielen Kopplungen von Fuzzy-Systemen mit anderen Methoden des Soft Computing eine immer grosere Rolle.
Detlef Nauck, Rudolf Kruse
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1993
Bei der Erstellung von Fuzzy Systemen ist ein auf Expertenwissen basierender oder heuristischer Ansatz zur Modellierung der zugehorigen Steuerregeln notwendig. Zur Erstellung von Adaptiven Fuzzy Systemen bietet der Einsatz von Neuronalen Netzwerken eine methodische Alternative zum Entwurf und zur Optimierung der Fuzzy-SystemParameter.
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Bei der Erstellung von Fuzzy Systemen ist ein auf Expertenwissen basierender oder heuristischer Ansatz zur Modellierung der zugehorigen Steuerregeln notwendig. Zur Erstellung von Adaptiven Fuzzy Systemen bietet der Einsatz von Neuronalen Netzwerken eine methodische Alternative zum Entwurf und zur Optimierung der Fuzzy-SystemParameter.
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Adaptive Neuro-Fuzzy Interference System
2015This chapter explains in detail the theoretical background of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The detailed explanation of this method will highlight its importance in the estimation of ZTD model.
Wayan Suparta, Kemal Maulana Alhasa
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Compromise Weighted Neuro — Fuzzy Systems
2003In the paper we present new neuro — fuzzy systems. They are called the AND-type fuzzy inference systems (NFIS). Based on the input — output data we learn not only parameters of membership functions but also a type of the systems and aggregating parameters. We propose the weighted T-norm and S-norm to neuro — fuzzy inference systems.
Leszek Rutkowski, Krzysztof Cpałka
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2010
Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability.
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Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability.
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Rough subspace neuro-fuzzy system
Fuzzy Sets and Systems, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Evolving Neuro-Fuzzy Inference Systems
2003Some knowledge-based fuzzy neural network models for on-line learning, such as EFuNN and FuzzyARTMAP, were presented in the previous chapter. Fuzzy neural networks are connectionist models that are trained as neural networks, but their structure can be interpreted as a set of fuzzy rules.
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