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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications (Signals and Communication Technology) - Softcover
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Neurofuzzy and fuzzyneural techniques as tools of studying and analyzing complex problems are relatively new even though neural networks and fuzzy logic systems have been applied as computational intelligence structural e- ments for the last 40 years. Computational intelligence as an independent sci- tific field has grown over the years because of the development of these str- tural elements. Neural networks have been revived since 1982 after the seminal work of J. J. Hopfield and fuzzy sets have found a variety of applications since the pub- cation of the work of Lotfi Zadeh back in 1965. Artificial neural networks (ANN) have a large number of highly interconnected processing elements that usually operate in parallel and are configured in regular architectures. The c- lective behavior of an ANN, like a human brain, demonstrates the ability to learn,recall,and generalize from training patterns or data. The performance of neural networks depends on the computational function of the neurons in the network,the structure and topology of the network,and the learning rule or the update rule of the connecting weights. This concept of trainable neural n- works further strengthens the idea of utilizing the learning ability of neural networks to learn the fuzzy control rules,the membership functions and other parameters of a fuzzy logic control or decision systems,as we will explain later on,and this becomes the advantage of using a neural based fuzzy logic system in our analysis. On the other hand,fuzzy systems are structured numerical estimators.
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This highly interdisciplinary book covers for the first time the applications of neurofuzzy and fuzzyneural scientific tools in a very wide area within the communications field. It deals with the important and modern areas of telecommunications amenable to such a treatment. Therefore, it is of interest to researchers and graduate students as well as practising engineers.
Integration of Neural and Fuzzy
Neuro-Fuzzy Applications in Speech Coding and Recognition
Image/Video Compression Using Neuro-Fuzzy Techniques
A Neuro-Fuzzy System for Source Location and Tracking in Wireless Communications
Fuzzy Neural Applications in Handoff
An Application of Neuro Fuzzy Systems for Access Control in Asynchronous Transfer Mode Networks
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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications (Signals and Communication Technology)
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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications (Signals and Communication Technology)
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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Neurofuzzy and fuzzyneural techniques as tools of studying and analyzing complex problems are relatively new even though neural networks and fuzzy logic systems have been applied as computational intelligence structural e- ments for the last 40 years. Computational intelligence as an independent sci- tific field has grown over the years because of the development of these str- tural elements. Neural networks have been revived since 1982 after the seminal work of J. J. Hopfield and fuzzy sets have found a variety of applications since the pub- cation of the work of Lotfi Zadeh back in 1965. Artificial neural networks (ANN) have a large number of highly interconnected processing elements that usually operate in parallel and are configured in regular architectures. The c- lective behavior of an ANN, like a human brain, demonstrates the ability to learn,recall,and generalize from training patterns or data. The performance of neural networks depends on the computational function of the neurons in the network,the structure and topology of the network,and the learning rule or the update rule of the connecting weights. This concept of trainable neural n- works further strengthens the idea of utilizing the learning ability of neural networks to learn the fuzzy control rules,the membership functions and other parameters of a fuzzy logic control or decision systems,as we will explain later on,and this becomes the advantage of using a neural based fuzzy logic system in our analysis. On the other hand,fuzzy systems are structured numerical estimators. 360 pp. Englisch. Bestandsnummer des Verkäufers 9783642622816
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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First book on neuro-fuzzy applications in the communications areaFor the first time, this highly interdisciplinary book covers the applications of neuro-fuzzy and fuzzy-neural scientific tools in a very wide area within the communications fiel. Bestandsnummer des Verkäufers 5064680
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Taschenbuch. Zustand: Neu. Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications | Peter Stavroulakis | Taschenbuch | xviii | Englisch | 2012 | Springer | EAN 9783642622816 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Bestandsnummer des Verkäufers 105986014
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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications
ISBN 10: 364262281X
ISBN 13: 9783642622816
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Neurofuzzy and fuzzyneural techniques as tools of studying and analyzing complex problems are relatively new even though neural networks and fuzzy logic systems have been applied as computational intelligence structural e- ments for the last 40 years. Computational intelligence as an independent sci- tific field has grown over the years because of the development of these str- tural elements. Neural networks have been revived since 1982 after the seminal work of J. J. Hopfield and fuzzy sets have found a variety of applications since the pub- cation of the work of Lotfi Zadeh back in 1965. Artificial neural networks (ANN) have a large number of highly interconnected processing elements that usually operate in parallel and are configured in regular architectures. The c- lective behavior of an ANN, like a human brain, demonstrates the ability to learn,recall,and generalize from training patterns or data. The performance of neural networks depends on the computational function of the neurons in the network,the structure and topology of the network,and the learning rule or the update rule of the connecting weights. This concept of trainable neural n- works further strengthens the idea of utilizing the learning ability of neural networks to learn the fuzzy control rules,the membership functions and other parameters of a fuzzy logic control or decision systems,as we will explain later on,and this becomes the advantage of using a neural based fuzzy logic system in our analysis. On the other hand,fuzzy systems are structured numerical estimators.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 360 pp. Englisch. Bestandsnummer des Verkäufers 9783642622816
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Neurofuzzy and fuzzyneural techniques as tools of studying and analyzing complex problems are relatively new even though neural networks and fuzzy logic systems have been applied as computational intelligence structural e- ments for the last 40 years. Computational intelligence as an independent sci- tific field has grown over the years because of the development of these str- tural elements. Neural networks have been revived since 1982 after the seminal work of J. J. Hopfield and fuzzy sets have found a variety of applications since the pub- cation of the work of Lotfi Zadeh back in 1965. Artificial neural networks (ANN) have a large number of highly interconnected processing elements that usually operate in parallel and are configured in regular architectures. The c- lective behavior of an ANN, like a human brain, demonstrates the ability to learn,recall,and generalize from training patterns or data. The performance of neural networks depends on the computational function of the neurons in the network,the structure and topology of the network,and the learning rule or the update rule of the connecting weights. This concept of trainable neural n- works further strengthens the idea of utilizing the learning ability of neural networks to learn the fuzzy control rules,the membership functions and other parameters of a fuzzy logic control or decision systems,as we will explain later on,and this becomes the advantage of using a neural based fuzzy logic system in our analysis. On the other hand,fuzzy systems are structured numerical estimators. Bestandsnummer des Verkäufers 9783642622816
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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications
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