Издательство CRC Press, 2010, -218 pp.
The problem of leaing in dynamic environments is important and challenging. In the 1960s, leaing from control of dynamical systems was studied extensively. At that time, leaing was similar in meaning to other terms such as adaptation and self-organizing. Since the 1970s, leaing theory has become a research discipline in the context of machine leaing, and more recently as computational or statistical leaing. As a result, leaing is considered as a problem of function estimation on the basis of empirical data, and leaing theory has been studied mainly by using statistical principles. Although many problems in leaing static nonlinear mappings have been handled successfully via statistical leaing, a leaing theory for dynamic systems, for example, leaing of the functional system dynamics from a dynamical process, has received much less investigation.
This book emphasizes leaing in uncertain dynamic environments, in which many aspects remain largely unexplored. The main subject of the monograph is knowledge acquisition, representation, and utilization in unknown dynamic processes. A deterministic framework is regarded as suitable for the intended purposes. Furthermore, this view comes naturally from deterministic algorithms in identification and adaptive control of nonlinear systems which motivate some of our work. Referred to as deterministic leaing (DL), the leaing theory presented gives promise of systematic design approaches for nonlinear system identification, dynamic patte recognition, and intelligent control of nonlinear systems.
Introduction
RBF Network Approximation and Persistence of Excitation
The Deterministic Leaing Mechanism
Deterministic Leaing from Closed-Loop Control
Dynamical Patte Recognition
Patte-Based Intelligent Control
Deterministic Leaing with Output Measurements
Toward Human-Like Leaing and Control
The problem of leaing in dynamic environments is important and challenging. In the 1960s, leaing from control of dynamical systems was studied extensively. At that time, leaing was similar in meaning to other terms such as adaptation and self-organizing. Since the 1970s, leaing theory has become a research discipline in the context of machine leaing, and more recently as computational or statistical leaing. As a result, leaing is considered as a problem of function estimation on the basis of empirical data, and leaing theory has been studied mainly by using statistical principles. Although many problems in leaing static nonlinear mappings have been handled successfully via statistical leaing, a leaing theory for dynamic systems, for example, leaing of the functional system dynamics from a dynamical process, has received much less investigation.
This book emphasizes leaing in uncertain dynamic environments, in which many aspects remain largely unexplored. The main subject of the monograph is knowledge acquisition, representation, and utilization in unknown dynamic processes. A deterministic framework is regarded as suitable for the intended purposes. Furthermore, this view comes naturally from deterministic algorithms in identification and adaptive control of nonlinear systems which motivate some of our work. Referred to as deterministic leaing (DL), the leaing theory presented gives promise of systematic design approaches for nonlinear system identification, dynamic patte recognition, and intelligent control of nonlinear systems.
Introduction
RBF Network Approximation and Persistence of Excitation
The Deterministic Leaing Mechanism
Deterministic Leaing from Closed-Loop Control
Dynamical Patte Recognition
Patte-Based Intelligent Control
Deterministic Leaing with Output Measurements
Toward Human-Like Leaing and Control