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Because they incorporate both time- and event-driven dynamics, stochastic hybrid systems (SHS) have become ubiquitous in a variety of fields, from mathematical finance to biological processes to communication networks to engineering. Comprehensively integrating numerous cutting-edge studies, Stochastic Hybrid Systems presents a captivating treatment of some of the most ambitious types of dynamic systems. Cohesively edited by leading experts in the field, the book introduces the theoretical basics, computational methods, and applications of SHS. It first discusses the underlying principles behind SHS and the main design limitations of SHS. Building on these fundamentals, the authoritative contributors present methods for computer calculations that apply SHS analysis and synthesis techniques in practice. The book concludes with examples of systems encountered in a wide range of application areas, including molecular biology, communication networks, and air traffic management. It also explains how to resolve practical problems associated with these systems. Stochastic Hybrid Systems achieves an ideal balance between a theoretical treatment of SHS and practical considerations. The book skillfully explores the interaction of physical processes with computerized equipment in an uncertain environment, enabling a better understanding of sophisticated as well as everyday devices and processes.
The general aim of this book is to provide an elementary exposition of some basic concepts in terms of which both classical and non-dassicallogirs may be studied and appraised. Although quantificational logic is dealt with briefly in the last chapter, the discussion is chiefly concemed with propo gjtional cakuli. Still, the subject, as it stands today, cannot br covered in one book of reasonable length. Rather than to try to include in the volume as much as possible, I have put emphasis on some selected topics. Even these could not be roverrd completely, but for each topic I have attempted to present a detailed and precise t'Xposition of several basic results including some which are non-trivial. The roots of some of the central ideas in the volume go back to J. Luka siewicz's seminar on mathematicallogi.
This carefully crafted ebook: "Selected Mathematical Works: Symbolic Logic + The Game of Logic + Feeding the Mind" is formatted for your eReader with a functional and detailed table of contents. Lewis Carroll wrote several mathematics books. He was mainly interested in using logic diagrams as a pedagogical tool. Symbolic Logic, first published in 1896, contains literally dozens of puzzles. He believed heartily that children would enjoy learning mathematics if they could be enticed by amusing stories and puzzles. The Game of Logic, published in 1897, was intended to teach logic to children. His "game" consisted of a card with two diagrams, together with a set of counters, five grey and four red. The two diagrams were Carroll's version of a two-set and a three-set Venn diagram. A manuscript of a brief lecture Lewis Carroll once gave, Feeding the Mind, discusses the importance of not only feeding the body, but also the mind. Carroll wittily puts forth connections between the diet of the body and mind, and gives helpful tips on how to best digest knowledge in the brain. This essay was originally printed in 1907. Lewis Carroll ((1832-1898) is best known as the author of Alice in Wonderland and Alice Through the Looking Glass. His real name was Charles Dodgson. His father, the Reverend Charles Dodgson, instilled in his son a love of mathematics from an early age. Lewis studied at Oxford, and later taught there as a Mathematics Lecturer.
Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition. Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes. This book is a valuable resource for students and teachers.
This book constitutes the refereed proceedings of the 2nd International Castle Meeting, ISMCTA 2008, Castillo de la Mota, Medina del Campo, Spain, September 2008. The 14 full papers and 5 invited papers presented were carefully reviewed and selected from 34 submissions for inclusion in the book. The papers cover network coding, quantum codes, group codes, codes and combinatorial structures, agebraic-geometry codes, as well as codes and applications.