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INDEPENDENT NEURO-FUZZY CONTROL SYSTEM
IFAC Proceedings Volumes, 2005Abstract The neuro fuzzy system based on two independent structures is described, the first a neuro-observer system developed by use of dynamical neural networks, and the second as the control system based on fuzzy logic system. These structures are described by independent way and their properties are analyzed.
Agustín I. Cabrera +2 more
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NEURO-FUZZY SYSTEMS: Learning Models
International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2006The goal of this research is the analysis of learning models by using of arithmetic operations applied in a neuro-fuzzy system (NFS). The research integrates the concepts between artificial neural network (ANN) and the fuzzy sets theory (FST). In order to assess the validity of the proposal, an FNS is proposed to diagnose paroxysmal events involving ...
L.F. de Carvalho +4 more
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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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