LEADER 03894oam 2200613zu 450 001 9911019096603321 005 20251116145954.0 010 $a9786610554539 010 $a9780470845356 010 $a047084535X 010 $a9781280554537 010 $a1280554533 035 $a(CKB)111056485558370 035 $a(SSID)ssj0000080489 035 $a(PQKBManifestationID)11120466 035 $a(PQKBTitleCode)TC0000080489 035 $a(PQKBWorkID)10096290 035 $a(PQKB)11526654 035 $a(MiAaPQ)EBC5247684 035 $a(Au-PeEL)EBL5247684 035 $a(CaONFJC)MIL55453 035 $a(OCoLC)1027204835 035 $a(Exl-AI)5247684 035 $a(EXLCZ)99111056485558370 100 $a20160829d2001 uy 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecurrent neural networks for prediction /$fDanilo P. Mandic, Jonathon A. Chambers 210 1$aChichester :$cJohn Wiley & Sons Ltd.,$d[2001] 215 $a1 online resource (280 pages) 225 1 $aWiley series in adaptive and learning systems for signal processing , communications, and control 300 $aSubtitle on cover: Learning algorithms, architectures and stability. 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9780471495178 311 08$a0471495174 311 08$a9780470852392 311 08$a0470852399 330 $aNew technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters. ? Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectures together with the concepts of modularity and nesting ? Examines stability and relaxation within RNNs ? Presents on-line learning algorithms for nonlinear adaptive filters and introduces new paradigms which exploit the concepts of a priori and a posteriori errors, data-reusing adaptation, and normalisation ? Studies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration ? Describes strategies for the exploitation of inherent relationships between parameters in RNNs ? Discusses practical issues such as predictability and nonlinearity detecting and includes several practical applications in areas such as air pollutant modelling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing Recurrent Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and, consequently, will have instant appeal. It provides an extensive background for researchers, academics and postgraduates enabling them to apply such networks in new applications. VISIT OUR COMMUNICATIONS TECHNOLOGY WEBSITE! http://www.wiley.co.uk/commstech/ VISIT OUR WEB PAGE! 330 8 $ahttp://www.wiley.co.uk/. 410 0$aWiley series in adaptive and learning systems for signal processing , communications, and control 606 $aNeural networks (Computer science)$7Generated by AI 606 $aMachine learning$7Generated by AI 615 0$aNeural networks (Computer science) 615 0$aMachine learning 676 $a6.32 700 $aMandic$b Danilo P.$00 801 0$bPQKB 906 $aBOOK 912 $a9911019096603321 996 $aRecurrent neural networks for prediction$94417234 997 $aUNINA