Results 121 to 130 of about 5,475 (153)
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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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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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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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Neuro-fuzzy identification models
Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482), 2005The paper deals with the neural net and fuzzy models as universal approximators. Four types of models suitable for identification are presented: the nonlinear output error, the nonlinear input error, the nonlinear generalised output error and the nonlinear generalised input error model.
D. Matko, R. Karba, B. Zupancic
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2001
One of the most important research themes, in the sense of intelligent processing techniques hybridization, is the neuro-fuzzy approach. The birth of this kind of system is mostly connected with the attempt to unify the advantages of neural and fuzzy techniques using one hybrid architecture only, often referred to as fuzzy neural networks (FNN).
Luigi Fortuna +5 more
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One of the most important research themes, in the sense of intelligent processing techniques hybridization, is the neuro-fuzzy approach. The birth of this kind of system is mostly connected with the attempt to unify the advantages of neural and fuzzy techniques using one hybrid architecture only, often referred to as fuzzy neural networks (FNN).
Luigi Fortuna +5 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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Neuro-Fuzzy Pattern Recognition
2000Methodology: simultaneous feature analysis and system identification in a neuro-fuzzy framework, N.R. Pal and D. Chakraborty neuro-fuzzy model for unsupervised feature extraction with real-life applications, R.K. De et al a computational-intelligence-based approach to decision support, M.B.
H Bunke, A Kandel
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1995
Neuronale Netze sind technische Abbilder von Nervensystemen, wie sie wesentlich fur die Gehirnfunktionen des Menschen (und naturlich auch anderer, hoher oder weniger hoch entwickelter Spezies) von Bedeutung sind. Ein Nervensystem besteht aus einer Vielzahl von miteinander “kommunizierenden” Nervenzellen, die als Neuronen bezeichnet werden.
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Neuronale Netze sind technische Abbilder von Nervensystemen, wie sie wesentlich fur die Gehirnfunktionen des Menschen (und naturlich auch anderer, hoher oder weniger hoch entwickelter Spezies) von Bedeutung sind. Ein Nervensystem besteht aus einer Vielzahl von miteinander “kommunizierenden” Nervenzellen, die als Neuronen bezeichnet werden.
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2009
Fuzzy logic became the core of a different approach to computing. Whereas traditional approaches to computing were precise, or hard edged, fuzzy logic allowed for the possibility of a less precise or softer approach (Klir et al., 1995, pp. 212-242). An approach where precision is not paramount is not only closer to the way humans thought, but may be in
Larbi Esmahi +2 more
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Fuzzy logic became the core of a different approach to computing. Whereas traditional approaches to computing were precise, or hard edged, fuzzy logic allowed for the possibility of a less precise or softer approach (Klir et al., 1995, pp. 212-242). An approach where precision is not paramount is not only closer to the way humans thought, but may be in
Larbi Esmahi +2 more
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The neuro-fuzzy system has gotten the great attention of researchers in numerous scientific areas due to its practical reasoning and learning capabilities. This chapter aims to help researchers to get a brief overview of the fuzzy sets and the related operations.
Zeinalnezhad, Masoomeh +2 more
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Zeinalnezhad, Masoomeh +2 more
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