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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
EUR 28,68 per la spedizione da Regno Unito a U.S.A.
Destinazione, tempi e costiEUR 3,43 per la spedizione in U.S.A.
Destinazione, tempi e costiDa: Lucky's Textbooks, Dallas, TX, U.S.A.
Condizione: New. Codice articolo ABLIING23Mar3113020231225
Quantità: Più di 20 disponibili
Da: Ria Christie Collections, Uxbridge, Regno Unito
Condizione: New. In. Codice articolo ria9783642622816_new
Quantità: Più di 20 disponibili
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: 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. Codice articolo 9783642622816
Quantità: 2 disponibili
Da: moluna, Greven, Germania
Condizione: 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. Codice articolo 5064680
Quantità: Più di 20 disponibili
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 360. Codice articolo 2658592171
Quantità: 4 disponibili
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Taschenbuch. Condizione: 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. Codice articolo 9783642622816
Quantità: 1 disponibili
Da: AHA-BUCH GmbH, Einbeck, Germania
Taschenbuch. Condizione: 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. Codice articolo 9783642622816
Quantità: 1 disponibili
Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. Print on Demand pp. 360 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam. Codice articolo 50967668
Quantità: 4 disponibili
Da: Biblios, Frankfurt am main, HESSE, Germania
Condizione: New. PRINT ON DEMAND pp. 360. Codice articolo 1858592161
Quantità: 4 disponibili
Da: Mispah books, Redhill, SURRE, Regno Unito
Paperback. Condizione: Like New. Like New. book. Codice articolo ERICA773364262281X6
Quantità: 1 disponibili