This book introduces classical and modern identification theories and methods, discusses various identification techniques such as neural networks and genetic neural network algorithms, and introduces methods for inducing and identifying chaos. Preface Chapter 1 Basic Concepts of Identification Chapter 2 Theoretical Foundations of Identification and Classical Identification Methods Chapter 3 Least Squares Parameter Identification Chapter 4 Gradient Correction Parameter Identification Chapter 5 Maximum Likelihood Identification Method Chapter 6 Adaptive Filtering for Discrete Random Systems Chapter 7 Neural Network Model Identification Chapter 8 Other Identification Methods for Nonlinear Dynamic Systems References
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