{"product_id":"recurrent-neural-networks-for-prediction-isbn-9780471495178","title":"Recurrent Neural Networks for Prediction","description":"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. \u003cul\u003e \u003cli\u003eAnalyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectures together with the concepts of modularity and nesting\u003c\/li\u003e \u003cli\u003eExamines stability and relaxation within RNNsPresents 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\u003c\/li\u003e \u003cli\u003eStudies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration\u003c\/li\u003e \u003cli\u003eDescribes strategies for the exploitation of inherent relationships between parameters in RNNs\u003c\/li\u003e \u003cli\u003eDiscusses 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\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eRecurrent Neural Networks for Prediction\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eVISIT OUR COMMUNICATIONS TECHNOLOGY WEBSITE! \u003cbr\u003ehttp:\/\/www.wiley.co.uk\/commstech\/\u003c\/p\u003e \u003cp\u003eVISIT OUR WEB PAGE! \u003cbr\u003ehttp:\/\/www.wiley.co.uk\/\u003c\/p\u003eDurch die Anwendung rückbezüglicher neuronaler Netze läßt sich die Leistungsfähigkeit konventioneller Technologien der digitalen Datenverarbeitung signifikant erhöhen. Von besonderer Bedeutung ist dies für komplexe Aufgaben, wie z.B. die mobile Kommunikation, die Robotik und die Medizintechnik. Das Buch faßt Originalarbeiten zur Stabilität neuronaler Netze zusammen und verbindet streng mathematische Analysen mit anschaulichen Anwendungen und experimentellen Belegen.  Preface.\u003cbr\u003e \u003cbr\u003e Introduction.\u003cbr\u003e \u003cbr\u003e Fundamentals.\u003cbr\u003e \u003cbr\u003e Network Architectures for Prediction.\u003cbr\u003e \u003cbr\u003e Activation Functions Used in Neural Networks.\u003cbr\u003e \u003cbr\u003e Recurrent Neural Networks Architectures.\u003cbr\u003e \u003cbr\u003e Neural Networks as Nonlinear Adaptive Filters.\u003cbr\u003e \u003cbr\u003e Stability Issues in RNN Architectures.\u003cbr\u003e \u003cbr\u003e Data-Reusing Adaptive Learning Algorithms.\u003cbr\u003e \u003cbr\u003e A Class of Normalised Algorithms for Online Training of Recurrent Neural Networks.\u003cbr\u003e \u003cbr\u003e Convergence of Online Learning Algorithms in Neural Networks.\u003cbr\u003e \u003cbr\u003e Some Practical Considerations of Predictability and Learning Algorithms for Various Signals.\u003cbr\u003e \u003cbr\u003e Exploiting Inherent Relationships Between Parameters in Recurrent Neural Networks.\u003cbr\u003e \u003cbr\u003e Appendix A: The O Notation and Vector and Matrix Differentiation.\u003cbr\u003e \u003cbr\u003e Appendix B: Concepts from the Approximation Theory.\u003cbr\u003e \u003cbr\u003e Appendix C: Complex Sigmoid Activation Functions, Holomorphic Mappings and Modular Groups.\u003cbr\u003e \u003cbr\u003e Appendix D: Learning Algorithms for RNNs.\u003cbr\u003e \u003cbr\u003e Appendix E: Terminology Used in the Field of Neural Networks.\u003cbr\u003e \u003cbr\u003e Appendix F: On the A Posteriori Approach in Science and Engineering.\u003cbr\u003e \u003cbr\u003e Appendix G: Contraction Mapping Theorems.\u003cbr\u003e \u003cbr\u003e Appendix H: Linear GAS Relaxation.\u003cbr\u003e \u003cbr\u003e Appendix I: The Main Notions in Stability Theory.\u003cbr\u003e \u003cbr\u003e Appendix J: Deasonsonalising Time Series.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Index. \u003cp\u003e\u003cb\u003eDanilo Mandic\u003c\/b\u003e from the Imperial College London, London, UK was named Fellow of the Institute of Electrical and Electronics Engineers in 2013 for contributions to multivariate and nonlinear learning systems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJonathon A. Chambers\u003c\/b\u003e is the author of \u003ci\u003eRecurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability\u003c\/i\u003e, published by Wiley.\u003c\/p\u003e 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. \u003cul\u003e \u003cli\u003eAnalyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectures together with the concepts of modularity and nesting\u003c\/li\u003e \u003cli\u003eExamines stability and relaxation within RNNs\u003c\/li\u003e \u003cli\u003ePresents 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\u003c\/li\u003e \u003cli\u003eStudies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration\u003c\/li\u003e \u003cli\u003eDescribes strategies for the exploitation of inherent relationships between parameters in RNNs\u003c\/li\u003e \u003cli\u003eDiscusses 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\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eRecurrent Neural Networks for Prediction\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989927084261,"sku":"NP9780471495178","price":274.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780471495178.jpg?v=1761785937","url":"https:\/\/k12savings.com\/products\/recurrent-neural-networks-for-prediction-isbn-9780471495178","provider":"K12savings","version":"1.0","type":"link"}