Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The book is designed for researchers, students and practitioners interested in using fast and efficient iterative methods to approximate solutions of nonlinear equations. The following four major problems are addressed. Problem 1: Show that the iterates are well defined. Problem 2: concerns the convergence of the sequences generated by a process and the question of whether the limit points are, in fact solutions of the equation. Problem 3: concerns the economy of the entire operations. Problem 4: concerns with how to best choose a method, algorithm or software program to solve a specific type

Funktionalanalysis. --- Iteration. --- Iterative methods (Mathematics). --- Lehrbuch. --- Numerische Mathematik. --- Iterative methods (Mathematics) --- Engineering & Applied Sciences --- Applied Mathematics --- Numerical analysis. --- Mathematical analysis --- Iteration (Mathematics) --- Numerical analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Iterative processing is an important technique with numerous applications. Exploiting the power of factor graphs, this detailed survey provides a general framework for systematically developing iterative algorithms for digital receivers, and highlights connections between important algorithms. Starting with basic concepts in digital communications, progressively more complex ideas are presented and integrated resulting in the development of cutting-edge algorithms for iterative receivers. Real-world applications are covered in detail, including decoding for turbo and LDPC codes, and detection for multi-antenna and multi-user systems. This accessible framework will allow the reader to apply factor graphs to practical problems, leading to the design of new algorithms in applications beyond digital receivers. With many examples and algorithms in pseudo-code, this book is an invaluable resource for graduate students and researchers in electrical engineering and computer science, and for practitioners in the communications industry. Additional resources for this title are available online at www.cambridge.org/9780521873154.

Digital communications --- Iterative methods (Mathematics). --- Parallel algorithms. --- Mathematical models. --- Iterative methods (Mathematics) --- Parallel algorithms --- 621.391 --- Algorithms --- Communications, Digital --- Digital transmission --- Pulse communication --- Digital electronics --- Pulse techniques (Electronics) --- Telecommunication --- Digital media --- Signal processing --- 621.391 General questions of electrical communication engineering. Cybernetics. Information theory. Signal theory --- General questions of electrical communication engineering. Cybernetics. Information theory. Signal theory --- Iteration (Mathematics) --- Numerical analysis --- Mathematical models --- Digital techniques

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This monograph studies the design of robust, monotonically-convergent iterative learning controllers for discrete-time systems. Two key problems with the fundamentals of iterative learning control (ILC) design as treated by existing work are: first, many ILC design strategies assume nominal knowledge of the system to be controlled and; second, it is well-known that many ILC algorithms do not produce monotonic convergence, though in applications monotonic convergence is often essential. Iterative Learning Control takes account of the recently-developed comprehensive approach to robust ILC analysis and design established to handle the situation where the plant model is uncertain. Considering ILC in the iteration domain, it presents a unified analysis and design framework that enables designers to consider both robustness and monotonic convergence for typical uncertainty models, including parametric interval uncertainties, iteration-domain frequency uncertainty, and iteration-domain stochastic uncertainty. Topics include: • Use of a lifting technique to convert the two-dimensional ILC system, which has dynamics in both the time and iteration domains, into the supervector framework, which yields a one-dimensional system, with dynamics only in the iteration domain. • Development of iteration-domain uncertainty models in the supervector framework. • ILC design for monotonic convergence when the plant is subject to parametric interval uncertainty in its Markov matrix. • An algebraic H-infinity design methodology for ILC design when the plant is subject to iteration-domain frequency uncertainty. • Development of Kalman-filter-based ILC algorithms when the plant is subject to iteration-domain stochastic uncertainties. • Analytical determination of the base-line error of ILC algorithms. • Solutions to three fundamental robust interval computational problems (used as basic tools for designing robust ILC controllers): finding the maximum singular value of an interval matrix, determining the robust stability of interval polynomial matrix, and obtaining the power of an interval matrix. Iterative Learning Control will be of great interest to academic researchers in control theory and to industrial control engineers working in robotics-oriented manufacturing and batch-processing-based industries. Graduate students of intelligent control will also find this volume instructive.

Engineering. --- Artificial intelligence. --- Bioinformatics. --- System theory. --- Control engineering. --- Robotics. --- Mechatronics. --- Biomedical engineering. --- Control. --- Systems Theory, Control. --- Artificial Intelligence (incl. Robotics). --- Control, Robotics, Mechatronics. --- Biomedical Engineering. --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- Mechanical engineering --- Microelectronics --- Microelectromechanical systems --- Automation --- Machine theory --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Programmable controllers --- Systems, Theory of --- Systems science --- Science --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Philosophy --- Data processing --- Intelligent control systems. --- Iterative methods (Mathematics) --- Iteration (Mathematics) --- Numerical analysis --- Intelligent control --- Intelligent controllers --- Automatic control --- Control and Systems Theory. --- Artificial Intelligence. --- Biomedical Engineering and Bioengineering. --- Systems theory.