9780792382904 - predictive modular neural networks: applications to time series (the springer international series in engineering and computer science, band 466) von petridis, vassilios; kehagias, athanasios (4 Ergebnisse)
Sprache: Englisch
Verlag: Springer 1998
Serie: The Springer International Series in Engineering and Computer Science, Buch 95 von 260. Buch 95 von 260 - The Springer International Series in Engineering and Computer Science
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 115,06
EUR 13,81 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Zustand: New. In.
Sprache: Englisch
Verlag: Kluwer Academic Publishers 1998
Serie: The Springer International Series in Engineering and Computer Science, Buch 95 von 260. Buch 95 von 260 - The Springer International Series in Engineering and Computer Science
- Hardcover
Anbieter: Kennys Bookstore, Olney, MD, USAKennys Bookstore
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 170,71
EUR 9,15 VersandVersand innerhalb von USAAnzahl: 15 verfügbar
Zustand: New. This text presents a unified methodology for designing modular neural networks. A family of online algorithms for time series classification, prediction and identification are developed; and a rigorous mathematical analysis of their properties is provided. Series: The Springer International Series in Engineering an…d Computer Science. Num Pages: 325 pages, biography. BIC Classification: UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 19. Weight in Grams: 642. . 1998. Hardback. . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Springer US 1998
Serie: The Springer International Series in Engineering and Computer Science, Buch 95 von 260. Buch 95 von 260 - The Springer International Series in Engineering and Computer Science
- Hardcover
Anbieter: moluna, Greven, , Deutschlandmoluna
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 127,84
EUR 48,99 VersandVersand von Deutschland nach USAAnzahl: Mehr als 20 verfügbar
Gebunden. Zustand: New.
Sprache: Englisch
Verlag: Springer Us Sep 1998 1998
Serie: The Springer International Series in Engineering and Computer Science, Buch 95 von 260. Buch 95 von 260 - The Springer International Series in Engineering and Computer Science
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 157,86
EUR 63,32 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Buch. Zustand: Neu. Neuware - The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition…, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several 'subnetworks' (modules), which may perform the same or re lated tasks, and then use an 'appropriate' method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of 'lumped' or 'monolithic' networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.
