Deterministic Learning Theory for Identification, Recognition, and - Contro

Informationen zum Autor Cong Wang, David J. Hill Klappentext Offering a new perspective on a largely unexplored area of knowledge acquisition! this book provides systematic design approaches for the identification! control! and recognition of nonlinear systems in uncertain environments. It begins with an introduction to the concepts of deterministic learning theory! followed by a discussion of RBF networks. Subsequent chapters describe the conceptual theory of deterministic learning processes and address closed-loop feedback control processes. Deterministic Learning Theory for Identification! Control! and Recognition also presents applications to areas such as fault detection! ECG/EEG pattern recognition! and security analysis. Zusammenfassung Provides systematic design approaches for the identification, control, and recognition of nonlinear systems in uncertain environments. This book introduces the concepts of deterministic learning theory and then discusses the persistent excitation property of RBF networks. Inhaltsverzeichnis Introduction. RBF Networks and the PE Condition. Locally Accurate Identification of Nonlinear Systems. Learning from Closed-Loop Neural Control. Rapid Recognition of Dynamical Patterns. Deterministic Learning using Output Measurements. Applications of Deterministic Learning. Conclusions.

Product details

• Authors: David J. Hill, Hill David J., Cong Wang, Cong (School of Automation Wang, Cong Hill Wang, Wang Cong
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 21.07.2009
• EAN: 9780849375538
• ISBN: 978-0-8493-7553-8
• No. of pages: 207
• Series: Automation and Control Engineering
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > General, dictionaries; Expert systems / knowledge-based systems, COMPUTERS / Artificial Intelligence / Expert Systems