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Añadir al carritoCondición: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
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Añadir al carritoHardcover. 339 S. Ehem. Bibliotheksexemplar mit Bib.-Signatur und Stempel in GUTEM Zustand. Kaum Gebrauchsspuren. 9783540407591 Sprache: Englisch Gewicht in Gramm: 550.
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Añadir al carritohardcover. Condición: Gut. 357 Seiten; 9783540407591.3 Gewicht in Gramm: 1.
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Idioma: Inglés
Publicado por Springer Berlin Heidelberg, 2004
ISBN 10: 3540407596 ISBN 13: 9783540407591
Librería: moluna, Greven, Alemania
EUR 178,14
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Añadir al carritoGebunden. Condición: New. 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.
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Idioma: Inglés
Publicado por Springer, Berlin, Springer, 2004
ISBN 10: 3540407596 ISBN 13: 9783540407591
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 219,80
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Añadir al carritoBuch. Condición: Neu. 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.