Recurrent Neural Networks Prediction by Mandic Danilo (22 results)

Condition: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,800grams, ISBN:9780471495178.

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.

hardcover. Condition: Sehr gut. Buch ist leicht verlagert (längs durchgebogen), kleine Lagerspuren am Buch, Inhalt einwandfrei und ungelesen 238113 Sprache: Englisch Gewicht in Gramm: 740.

Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

hardcover. Condition: New. In shrink wrap. Looks like an interesting title!

Hardcover. Condition: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New. In.

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.

Condition: As New. Unread book in perfect condition.

Condition: New.

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.

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. . . . .

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.

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.

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.

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

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