9780849375538 - Deterministic Learning Theory for Identification, Recognition, and Control di Wang, Cong; Hill, David J. (18 risultati)

HARDCOVER. Condizione: New.

Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In.

Condizione: As New. Unread book in perfect condition.

Condizione: New. 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. Series Editor(s): Lewis, Frank L. Series: Automation and Control Engineering. Num Pages: 207 pages, 147 black & white illustrations. BIC Classification: UYQE. Category: (P) Professional & Vocational. Dimension: 243 x 164 x 20. Weight in Grams: 508. . 2009. Hardback. . . . .

Hardback. Condizione: New. Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way. A Deterministic View of Learning in Dynamic EnvironmentsThe authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems. A New Model of Information ProcessingThis book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).

Condizione: New. Cong Wang, David J. HillDeterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic envi.

Condizione: New. 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. Series Editor(s): Lewis, Frank L. Series: Automation and Control Engineering. Num Pages: 207 pages, 147 black & white illustrations. BIC Classification: UYQE. Category: (P) Professional & Vocational. Dimension: 243 x 164 x 20. Weight in Grams: 508. . 2009. Hardback. . . . . Books ship from the US and Ireland.

Condizione: New. Brand New ! Fast Delivery "International Edition " and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

Hardback. Condizione: New. Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way. A Deterministic View of Learning in Dynamic EnvironmentsThe authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems. A New Model of Information ProcessingThis book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).

Hardcover. Condizione: Brand New. 1st edition. 207 pages. 9.53x6.30x0.71 inches. In Stock.

Hardcover. Condizione: Brand New. 1st edition. 207 pages. 9.53x6.30x0.71 inches. In Stock. This item is printed on demand.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Offering a new perspective, this book provides systematic design approaches for the identification, control, and recognition of nonlinear systems in uncertain environments. It introduces the concepts of deterministic learning theory and then discusses the persistent excitation property of RBF networks. The authors describe the theory of deterministic learning processes and address dynamical pattern recognition and pattern-based control processes. They present a new model of dynamical parallel distributed processing applicable to the detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems.

Buch. Condizione: Neu. Deterministic Learning Theory for Identification, Recognition, and Control | Cong Wang (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2009 | CRC Press | EAN 9780849375538 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.