Recurrent Neural Networks Prediction (28 results)

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Hardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. C 1202: (2001) 9780470845356 Sprache: Englisch Gewicht in Gramm: 1150.

Hardcover. Condition: Very Good. No Jacket. 1st Edition. 2001.Hardcover.Very good condition.285 pages.Ships from Japan.Usually ships in 1-2 working days.

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Hardcover. Condition: new. Hardcover. 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 nestingExamines 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 normalisationStudies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iterationDescribes strategies for the exploitation of inherent relationships between parameters in RNNsDiscusses 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! VISIT OUR WEB PAGE! / Neural networks consist of interconnected groups of neurones which function as processing units. Through the application of neural networks, the capabilities of conventional digital signal processing techniques can be significantly enhanced to meet the demands of new technologies such as mobile communications, robotics and medical instrumentation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condition: New. pp. xxi + 285 Illus.

Hardcover. Condition: new. Hardcover. 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 nestingExamines 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 normalisationStudies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iterationDescribes strategies for the exploitation of inherent relationships between parameters in RNNsDiscusses 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! VISIT OUR WEB PAGE! / Neural networks consist of interconnected groups of neurones which function as processing units. Through the application of neural networks, the capabilities of conventional digital signal processing techniques can be significantly enhanced to meet the demands of new technologies such as mobile communications, robotics and medical instrumentation. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: new. Hardcover. 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 nestingExamines 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 normalisationStudies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iterationDescribes strategies for the exploitation of inherent relationships between parameters in RNNsDiscusses 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! VISIT OUR WEB PAGE! / Neural networks consist of interconnected groups of neurones which function as processing units. Through the application of neural networks, the capabilities of conventional digital signal processing techniques can be significantly enhanced to meet the demands of new technologies such as mobile communications, robotics and medical instrumentation. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Gebunden. Condition: New. Neural networks consist of interconnected groups of neurons which function as processing units and aim to reconstruct the operation of the human brain.InhaltsverzeichnisPreface. Introduction. Fundamentals. Network Architectures fo.

Condition: New. pp. xxi + 285 1st Edition.

Condition: New. Neural networks consist of interconnected groups of neurons which function as processing units and aim to reconstruct the operation of the human brain. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 308 pages, Ill. BIC Classification: TJK; UYQN; UYS. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 250 x 175 x 23. Weight in Grams: 720. . 2001. 1st Edition. Hardcover. . . . .

Condition: New. Neural networks consist of interconnected groups of neurons which function as processing units and aim to reconstruct the operation of the human brain. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 308 pages, Ill. BIC Classification: TJK; UYQN; UYS. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 250 x 175 x 23. Weight in Grams: 720. . 2001. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Condition: Neu. Neuware - 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.

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Hardcover. Condition: Brand New. 285 pages. 9.75x6.75x1.00 inches. In Stock.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. Recurrent Neural Networks for Sequence Prediction is a cutting-edge guide to the application of recurrent neural networks (RNNs) in sequence prediction tasks. By examining the principles of RNN architecture, training, and optimization, this book provides a comprehensive understanding of the complex relationships between neural networks, sequence data, and prediction accuracy. With a focus on real-world examples and practical case studies, Recurrent Neural Networks for Sequence Prediction is an essential resource for anyone working with sequence data and looking to improve their predictive modeling skills. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. Recurrent neural networks have revolutionized the field of time series prediction, enabling accurate forecasting of complex systems. This book provides a thorough introduction to the fundamental concepts and architectures of recurrent neural networks, including long short-term memory (LSTM) and gated recurrent units (GRU). Through a combination of theoretical explanations and practical examples, readers will gain a deep understanding of how to design and implement effective recurrent neural networks for time series prediction. From basic to advanced topics, this book covers the essential techniques and tools needed to tackle real-world problems. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. Recurrent neural networks (RNNs) are a type of deep learning algorithm that's revolutionizing the field of time series prediction. Using Recurrent Neural Networks for Time Series Prediction provides a comprehensive guide to understanding the principles and applications of RNNs, from forecasting stock prices to predicting weather patterns. By mastering the art of RNNs, you'll learn how to develop sophisticated time series prediction systems that can analyze and understand complex data with unprecedented accuracy. This book will walk you through the process of designing, training, and deploying RNNs for a wide range of applications, from finance to healthcare. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. Using Recurrent Neural Networks for Sequence Prediction provides a thorough introduction to the principles and applications of recurrent neural networks (RNNs) in sequence prediction tasks. By exploring the architecture, training, and optimization of RNNs, this book helps readers understand how to design and implement effective sequence prediction systems that can accurately forecast and classify sequential data. With its focus on theoretical foundations and practical applications, Using Recurrent Neural Networks for Sequence Prediction is an essential resource for anyone working in natural language processing, time series analysis, or predictive modeling. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Hardcover. Condition: Brand New. 285 pages. 9.75x6.75x1.00 inches. In Stock. This item is printed on demand.