Deterministic Learning Theory for Identification, Recognition, and Control

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.