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Neuro-Fuzzy Systems and their Applications
2008 7th Computer Information Systems and Industrial Management Applications, 2008Summary form only given. In the lecture, we incorporate various flexibility parameters to the construction of neuro-fuzzy systems. This approach dramatically improves their performance, allowing the systems to perfectly represent the pattern encoded in data.
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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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Design of experiments in neuro-fuzzy systems
Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005Interest in hybrid methods that combine artificial neural networks and fuzzy inference systems has grown in recent years. These systems are robust solutions that search for representations of domain knowledge, reasoning on uncertainty, automatic learning and adaptation.
Cleber Zanchettin +2 more
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1998
This paper is about so-called neuro-fuzzy systems, which combine methods from neural network theory with fuzzy systems. Such combinations have been considered for several years already. However, the term neuro-fuzzy still lacks proper definition, and still has the flavour of a buzzword to it.
Rudolf Kruse, Detlef Nauck
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This paper is about so-called neuro-fuzzy systems, which combine methods from neural network theory with fuzzy systems. Such combinations have been considered for several years already. However, the term neuro-fuzzy still lacks proper definition, and still has the flavour of a buzzword to it.
Rudolf Kruse, Detlef Nauck
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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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Proceedings of IEEE 5th International Fuzzy Systems, 2002
Neural VLSI devices are now available and it would be interesting to use them for logical operations. We show that, in the Lukasiewicz logic, it is possible to use an artificial neuron to implement four basic logical operators (conjunction, disjunction, implication and negation). A new operator, AND-OR, is introduced with the same formalism. Finally, a
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Neural VLSI devices are now available and it would be interesting to use them for logical operations. We show that, in the Lukasiewicz logic, it is possible to use an artificial neuron to implement four basic logical operators (conjunction, disjunction, implication and negation). A new operator, AND-OR, is introduced with the same formalism. Finally, a
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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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2013
Performance improvement of fuzzy logic controllers (FLC) can be achieved by adjusting the membership functions (MF). Neuro-fuzzy approaches are mostly used in such adjustment procedure, which involves several parameters of the MFs to be adjusted. In many cases, tuning the scaling factors gives the same performance as with MFs adjustment.
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Performance improvement of fuzzy logic controllers (FLC) can be achieved by adjusting the membership functions (MF). Neuro-fuzzy approaches are mostly used in such adjustment procedure, which involves several parameters of the MFs to be adjusted. In many cases, tuning the scaling factors gives the same performance as with MFs adjustment.
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1998
Neuro-fuzzy classification systems offer means to obtain fuzzy classification rules by a learning algorithm. It is usually possible to find a suitable fuzzy classifier by learning from data, but it can be hard to obtain a classifier that can be interpreted conveniently.
Detlef Nauck +2 more
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Neuro-fuzzy classification systems offer means to obtain fuzzy classification rules by a learning algorithm. It is usually possible to find a suitable fuzzy classifier by learning from data, but it can be hard to obtain a classifier that can be interpreted conveniently.
Detlef Nauck +2 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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