Author: Peter S. Maybeck
Publisher: Academic Press
Release Date: 1982-08-25
This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.
Author: David D'Argenio
Publisher: Springer Science & Business Media
Release Date: 2006-04-18
Advanced Methods of Pharmacokinetic and Pharmocodynamic Systems Analysis Volume 3 is vital to professionals and academicians working in drug development and bioengineering. Both basic and clinical scientists will benefit from this work. This book contains chapters by leading researchers in pharmacokinetic/pharmacodynamic modeling and will be of interest to anyone involved with the application of pharmacokinetic and pharmacodynamics to drug development. The use of mathematical modeling and associated computational methods is central to the study of the absorption, distribution and elimination of therapeutic drugs (pharmacokinetics) and to understanding how drugs produce their effects (pharmacodynamics). From its inception, the field of pharmacokinetics and pharmacodynamics has incorporated methods of mathematical modeling, simulation and computation in an effort to better understand and quantify the processes of uptake, disposition and action of therapeutic drugs. These methods for pharmacokinetic/pharmacodynamic systems analysis impact all aspects of drug development. In vitro, animal and human testing, as well as drug therapy are all influenced by these methods. Modeling methodologies developed for studying pharmacokinetic/ pharmacodynamic processes confront many challenges. This is related in part to the severe restrictions on the number and type of measurements that are available from laboratory experiments and clinical trials, as well as the variability in the experiments and the uncertainty associated with the processes themselves. The contributions are organized in three main areas: Mechanism-Based PK/PD, Pharmacometrics and Pharmacotherapy. Both professionals and academics will profit from this extensive work.
Author: Dan Simon
Publisher: John Wiley & Sons
Release Date: 2006-06-19
Genre: Technology & Engineering
A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.
This comprehensive introduction to the estimation and control of dynamic stochastic systems provides complete derivations of key results. The second edition includes improved and updated material, and a new presentation of polynomial control and new derivation of linear-quadratic-Gaussian control.
The book constitutes the refereed proceedings of the International Workshop on Algebraic Frames for the Perception-Action Cycle, AFPAC '97, held in Kiel, Germany, in September 1997. The volume presents 12 revised full papers carefully reviewed and selected for inclusion in the book. Also included are 10 full invited papers by leading researchers in the area providing a representative state-of-the-art assessment of this rapidly growing field. The papers are organized in topical sections on PAC systems, low level and early vision, recognition of visual structure, processing of 3D visual space, representation and shape perception, inference and action, and visual and motor neurocomputation.