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A presentation of techniques in advanced process modelling, identification, prediction, and parameter estimation for the implementation and analysis of industrial systems. The authors cover applications for the identification of linear and non-linear systems, the design of generalized predictive controllers (GPCs), and the control of multivariable systems.
This textbook provides a comprehensive and current understanding of signal detection and estimation, including problems and solutions for each chapter. Signal detection plays an important role in fields such as radar, sonar, digital communications, image processing, and failure detection. The book explores both Gaussian detection and detection of Markov chains, presenting a unified treatment of coding and modulation topics. Addresses asymptotic of tests with the theory of large deviations, and robust detection. This text is appropriate for students of Electrical Engineering in graduate courses in Signal Detection and Estimation.
System Identification is a special section of the International Federation of Automatic Control (IFAC)-Journal Automatica that contains tutorial papers regarding the basic methods and procedures utilized for system identification. Topics include modeling and identification; step response and frequency response methods; correlation methods; least squares parameter estimation; and maximum likelihood and prediction error methods. After analyzing the basic ideas concerning the parameter estimation methods, the book elaborates on the asymptotic properties of these methods, and then investigates the application of the methods to particular model structures. The text then discusses the practical aspects of process identification, which includes the usual, general procedures for process identification; selection of input signals and sampling time; offline and on-line identification; comparison of parameter estimation methods; data filtering; model order testing; and model verification. Computer program packages are also discussed. This compilation of tutorial papers aims to introduce the newcomers and non-specialists in this field to some of the basic methods and procedures used for system identification.
For dynamic distributed systems modeled by partial differential equations, existing methods of sensor location in parameter estimation experiments are either limited to one-dimensional spatial domains or require large investments in software systems. With the expense of scanning and moving sensors, optimal placement presents a critical problem.