Adaptation in Natural and Artificial Systems

An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
Author: John Henry Holland
Publisher: MIT Press
ISBN: 9780262581110
Category: Computers
Page: 211
View: 8830

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List of figures. Preface to the 1992 edition. Preface. The general setting. A formal framework. lustrations. Schemata. The optimal allocation of trials. Reproductive plans and genetic operators. The robustness of genetic plans. Adaptation of codings and representations. An overview. Interim and prospectus. Glossary of important symbols.

Neural Networks Theory


Author: Alexander I. Galushkin
Publisher: Springer Science & Business Media
ISBN: 3540481257
Category: Mathematics
Page: 396
View: 5412

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This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. It details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis. The theory is expansive: covering not just traditional topics such as network architecture but also neural continua in function spaces as well.

Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning


Author: Thanasis Daradoumis,Stavros N. Demetriadis,Fatos Xhafa
Publisher: Springer
ISBN: 3642285864
Category: Computers
Page: 336
View: 3951

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Adaptation and personalization have been extensively studied in CSCL research community aiming to design intelligent systems that adaptively support eLearning processes and collaboration. Yet, with the fast development in Internet technologies, especially with the emergence of new data technologies and the mobile technologies, new opportunities and perspectives are opened for advanced adaptive and personalized systems. Adaptation and personalization are posing new research and development challenges to nowadays CSCL systems. In particular, adaptation should be focused in a multi-dimensional way (cognitive, technological, context-aware and personal). Moreover, it should address the particularities of both individual learners and group collaboration. As a consequence, the aim of this book is twofold. On the one hand, it discusses the latest advances and findings in the area of intelligent adaptive and personalized learning systems. On the other hand it analyzes the new implementation perspectives for intelligent adaptive learning and collaborative systems that are brought by the advances in scripting languages, IMS LD, educational modeling languages and learning activity management systems. Given the variety of learning needs as well as the existence of different technological solutions, the book exemplifies the methodologies and best practices through several case studies and adaptive real-world collaborative learning scenarios, which show the advancement in the field of analysis, design and implementation of intelligent adaptive and personalized systems.

Advances in Information Systems Science


Author: Julius T. Tou
Publisher: Springer Science & Business Media
ISBN: 1461582431
Category: Juvenile Nonfiction
Page: 354
View: 5394

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Information systems science embraces a broad spectrum of topics. It is vir tually impossible to provide comprehensive and in-depth discussion, other than simple recitals of recent results, of every important topic in each volume of this annual review series. Since we have chosen the former approach, each volume will only cover certain aspects of recent advances in this bur geoning field. The emphasis in this volume, the third of a continuing series, is focussed upon pattern recognition, pictorial information manipulation, and new approaches to logical design of information networks. In Chapter 1, V. A. Kovalevsky presents a tutorial survey of practical and theoretical developments in pattern recognition. He categorizes the basic developments in three different directions. The first direction is charac terized by an empirical treatment with highly specialized recognition schemes. In the second direction, the major efforts are centered upon the cre ation of learning systems capable of improving recognition performance on the basis of past experience. The majority of the work in the third direction is devoted to the study of the basic structure of complex patterns, the con struction of mathematical models for pattern recognition, and the analysis of complex pictorial representations. The author elucidates the "heuristics" approach and the "science" approach to pattern recognition problems. This chapter together with Chapter 2 of this volume supplements the chapter on Engineering Principles of Pattern Recognition in Volume 1 to provide a more complete treatment of this subject.

Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications

Emerging Trends and Applications
Author: Chiong, Raymond
Publisher: IGI Global
ISBN: 1605667994
Category: Business & Economics
Page: 360
View: 2876

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"This volume offers intriguing applications, reviews and additions to the methodology of intelligent computing, presenting the emerging trends of state-of-the-art intelligent systems and their practical applications"--Provided by publisher.

Advances in Semantic Media Adaptation and Personalization


Author: Manolis Wallace,Marios C. Angelides,Phivos Mylonas
Publisher: Springer Science & Business Media
ISBN: 3540763597
Category: Computers
Page: 368
View: 8359

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Realizing the growing importance of semantic adaptation and personalization of media, the editors of this book brought together leading researchers and practitioners of the field to discuss the state-of-the-art, and explore emerging exciting developments. This volume comprises extended versions of selected papers presented at the 1st International Workshop on Semantic Media Adaptation and Personalization (SMAP 2006), which took place in Athens in December 2006.

Adaptation and Learning in Control and Signal Processing 2001

A Proceedings Volume from the IFAC Workshop, Cernobbio-Como, Italy, 29-31 August 2001
Author: Sergio Bittanti
Publisher: Pergamon
ISBN: N.A
Category: Technology & Engineering
Page: 492
View: 9414

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In control and signal processing, adaptation is a natural tool to cope with real-time changes in the dynamical behaviour of signals and systems. In this area, strongly connected with prediction and identification, there has been an increasing interest in switching and supervising methods. Moreover in recent years, special attention has been paid to the ideas evolving round the theory of statistical learning as a potential tool of improved adaptation. The IFAC workshop on Adaptation and Learning in Control and Signal Processing in 2001 gathered together experts in the field and interested researchers from universities and industry to present a full picture of the area. This proceedings volume presents papers covering the following subjects: Model reference and predictive control; Multiple model control; Adaptive control I/II; Adaptive control and learning; Learning; Adaptive control of nonlinear systems I/II; Supervisory control; Neural networks for control; PID design methods; Sliding mode; Adaptive filtering and estimation; Identification methods I/II.

Advanced Mathematical Tools for Automatic Control Engineers: Volume 2

Stochastic Systems
Author: Alex Poznyak
Publisher: Elsevier
ISBN: 9780080914039
Category: Technology & Engineering
Page: 567
View: 3974

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Advanced Mathematical Tools for Automatic Control Engineers, Volume 2: Stochastic Techniques provides comprehensive discussions on statistical tools for control engineers. The book is divided into four main parts. Part I discusses the fundamentals of probability theory, covering probability spaces, random variables, mathematical expectation, inequalities, and characteristic functions. Part II addresses discrete time processes, including the concepts of random sequences, martingales, and limit theorems. Part III covers continuous time stochastic processes, namely Markov processes, stochastic integrals, and stochastic differential equations. Part IV presents applications of stochastic techniques for dynamic models and filtering, prediction, and smoothing problems. It also discusses the stochastic approximation method and the robust stochastic maximum principle. Provides comprehensive theory of matrices, real, complex and functional analysis Provides practical examples of modern optimization methods that can be effectively used in variety of real-world applications Contains worked proofs of all theorems and propositions presented