Stochastic processes are those probabilistic tools used to approximate uncertainty. With the advent of the computer, uncertainty which is inherent in large complex systems can be approximated. Advances in space science, communications systems and many of the defence initiatives, not to mention robotics and artificial intelligence, have come about because of the ability of the computer to solve massive probabilistic calculations to account for uncertainty in the system being adjusted. This text applies the most recent advances in stochastic and probabilistic processes to areas such as communications and robotic technology. The author also uses the thermodynamic principle of entropy to measure and analyze uncertainty in various systems.
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GEORGE N. SARIDIS is Professor of Electrical Engineering and Director of the Robotics and Automation Laboratories at Rensselaer Polytechnic Institute. Recognized throughout the world as one of the foremost theoreticians in robotics, automation, and systems engineering, Professor Saridis has received numerous honors, including the Founding President Award of the IEEE and the IEEE Centennial Medal. He has served as Program Director of the Systems Theory and Applications Program in the Engineering Division of the National Science Foundation. Among his publications are Intelligent Robotic Systems: Theory, Design, and Applications (with K. P. Valavanis) and Self-Organizing Control of Stochastic Systems. Dr. Saridis is a member of the editorial board of Journal of Robotic Systems, published by Wiley.
The first new introduction to stochastic processes in 20 years incorporates a modern, innovative approach to estimation and control theory
Stochastic Processes, Estimation, and Control: The Entropy Approach provides a comprehensive, up–to–date introduction to stochastic processes, together with a concise review of probability and system theory. Serving both as a text for graduate courses in control and systems engineering and as a guide for the practicing engineer and researcher, the book applies the most recent advances in stochastic and probabilistic processes to such areas as communications systems and robotics technology.
Highlights of this outstanding text:
Since the early 18th century, scientists have devised probability measures to help them describe uncertain phenomena and approximate the actual laws of nature. It was not until the advent of the computer, however, that stochastic processes have made it possible to approximate the uncertainty inherent in large complex systems. The miracles of space exploration, modern defense systems, and artificial intelligence are just a few of the dramatic advances that have come about due to the ability of the modern digital computer to perform massive probabilistic calculations.
In this, the first introductory book on stochastic processes in twenty years, leading theoretician George Saridis provides a modern innovative approach that applies the most recent advances in probabilistic processes to such areas as communications and robotics technology.
Stochastic Processes, Estimation, and Control: The Entropy Approach is designed as a text for graduate courses in dynamic programming and stochastic control, stochastic processes, or applied probability in the engineering or mathematical/computational science departments, and as a guide for the practicing engineer and researcher. It offers a lucid discussion of parameter estimation based on least square techniques, an in–depth investigation of the estimation of the states of a stochastic linear and nonlinear dynamic system, and a modified derivation of the linear–quadratic Gaussian optimal control problem. Professor Saridis′s presentation of estimation and control theory is thorough, but avoids the use of advanced mathematics. A new theory of approximation of the optimal solution for nonlinear stochastic systems is presented as a general engineering tool, and the whole area of stochastic processes, estimation, and control is recast using entropy as a measure.
Stochastic Processes, Estimation, and Control: The Entropy Approach is the first book to apply the thermodynamic principle of entropy to the measurement and analysis of uncertainty in systems. Its new reformulation takes an important first step toward a unified approach to the theory of intelligent machines, where artificial intelligence and system theory come together.
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