03894oam 2200613zu 450 991101909660332120251116145954.097866105545399780470845356047084535X97812805545371280554533(CKB)111056485558370(SSID)ssj0000080489(PQKBManifestationID)11120466(PQKBTitleCode)TC0000080489(PQKBWorkID)10096290(PQKB)11526654(MiAaPQ)EBC5247684(Au-PeEL)EBL5247684(CaONFJC)MIL55453(OCoLC)1027204835(Exl-AI)5247684(EXLCZ)9911105648555837020160829d2001 uy engurcnu||||||||txtrdacontentcrdamediacrrdacarrierRecurrent neural networks for prediction /Danilo P. Mandic, Jonathon A. ChambersChichester :John Wiley & Sons Ltd.,[2001]1 online resource (280 pages)Wiley series in adaptive and learning systems for signal processing , communications, and controlSubtitle on cover: Learning algorithms, architectures and stability.Bibliographic Level Mode of Issuance: Monograph9780471495178 0471495174 9780470852392 0470852399 New 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!http://www.wiley.co.uk/.Wiley series in adaptive and learning systems for signal processing , communications, and controlNeural networks (Computer science)Generated by AIMachine learningGenerated by AINeural networks (Computer science)Machine learning6.32Mandic Danilo P.0PQKBBOOK9911019096603321Recurrent neural networks for prediction4417234UNINA