Description
Book SynopsisDeterministic 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 Environments
The 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 a
Table of Contents
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.
