Recurrent Neural Networks and Soft Computing

Mahmoud Elhefnawi

ISBN 10: 9535104098 / ISBN 13: 9789535104094

Verlag: Intechopen Mär 2012, 2012

Beschreibung

Neuware -New applications in recurrent neural networks are covered by this book, which will be required reading in the field. Methodological tools covered include ranking indices for fuzzy numbers, a neuro-fuzzy digital filter and mapping graphs of parallel programmes. The scope of the techniques profiled in real-world applications is evident from chapters on the recognition of severe weather patterns, adult and foetal ECGs in healthcare and the prediction of temperature time-series signals. Additional topics in this vein are the application of AI techniques to electromagnetic interference problems, bioprocess identification and I-term control and the use of BRNN-SVM to improve protein-domain prediction accuracy. Recurrent neural networks can also be used in virtual reality and nonlinear dynamical systems, as shown by two chapters.Books on Demand GmbH, Überseering 33, 22297 Hamburg 304 pp. Englisch.

Bestandsnummer des Verkäufers 9789535104094

Bibliografische Details

• Titel: Recurrent Neural Networks and Soft Computing
• Verlag: Intechopen Mär 2012
• Erscheinungsjahr: 2012
• Sprache: Englisch
• ISBN-10: 9535104098
• ISBN-13: 9789535104094
• Einband: Buch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

New applications in recurrent neural networks are covered by this book, which will be required reading in the field. Methodological tools covered include ranking indices for fuzzy numbers, a neuro-fuzzy digital filter and mapping graphs of parallel programmes. The scope of the techniques profiled in real-world applications is evident from chapters on the recognition of severe weather patterns, adult and foetal ECGs in healthcare and the prediction of temperature time-series signals. Additional topics in this vein are the application of AI techniques to electromagnetic interference problems, bioprocess identification and I-term control and the use of BRNN-SVM to improve protein-domain prediction accuracy. Recurrent neural networks can also be used in virtual reality and nonlinear dynamical systems, as shown by two chapters.