{"product_id":"recurrent-neural-networks-for-prediction-learning-algorithms-architectures-and-stability-hardback-9780471495178","title":"Recurrent Neural Networks for Prediction; Learning Algorithms, Architectures and Stability (Hardback) 9780471495178","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eRecurrent Neural Networks for Prediction\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eLearning Algorithms, Architectures and Stability\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eDanilo P. Mandic (Author), Jonathon A. Chambers (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471495178, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 6 August 2001\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e304 pages\u003cbr\u003e24.7 x 17.4 x 2.3 cm, 0.709 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface.\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.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52507274805528,"sku":"9780471495178","price":154.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780471495178.jpg?v=1786443180","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/recurrent-neural-networks-for-prediction-learning-algorithms-architectures-and-stability-hardback-9780471495178","provider":"Freshly Printed Books","version":"1.0","type":"link"}