Describes the theory, methodology, and practice of system identification, combining mathematics, real-world applications, and different types of exercises. More technical material is placed in marked sections and appendices. For a graduate course in system identification, for students familiar with dynamical systems and stochastic signals. This edition reflects changes in the field over the past 10 years, with new material on subspace methods, nonlinear black-box models, frequency-domain methods, and data preprocessing. Annotation c. by Book News, Inc., Portland, Or.