Intelligent Software for Chemical Analysis ( Volume 13 )

Publication series :Volume 13

Author: Buydens   L. M. C.;Schoenmakers   P. J.  

Publisher: Elsevier Science‎

Publication year: 1993

E-ISBN: 9780080868400

P-ISBN(Paperback): 9780444892072

P-ISBN(Hardback):  9780444892072

Subject: O65 Analytical Chemistry;TP Automation Technology , Computer Technology;TP1 自动化基础理论

Language: ENG

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Description

Various emerging techniques for automating intelligent functions in the laboratory are described in this book. Explanations on how systems work are given and possible application areas are suggested. The main part of the book is devoted to providing data which will enable the reader to develop and test his own systems. The emphasis is on expert systems; however, promising developments such as self-adaptive systems, neural networks and genetic algorithms are also described.


The book has been written by chemists with a great deal of practical experience in developing and testing intelligent software, and therefore offers first-hand knowledge. Laboratory staff and managers confronted with commercial intelligent software will find information on the functioning, possibilities and limitations thereof, enabling them to select and use modern software in an optimum fashion. Finally, computer scientists and information scientists will find a wealth of data on the application of contemporary artificial intelligence techniques.

Chapter

Front Cover

pp.:  1 – 4

Copyright Page

pp.:  5 – 14

Foreword

pp.:  6 – 8

Preface

pp.:  8 – 12

List of Contributors

pp.:  12 – 20

Contents

pp.:  14 – 6

Chapter 1. Introduction

pp.:  20 – 32

Chapter 2. Knowledge-based Systems in Chemical Analysis

pp.:  32 – 98

Chapter 3. Developing Expert Systems

pp.:  98 – 140

Chapter 4. Expert-System-Development Tools

pp.:  140 – 172

Chapter 5. Validation and Evaluation of Expert Systems for HPLC Met hod Development . Case Studies

pp.:  172 – 244

Chapter 6. Self-adaptive Expert Systems

pp.:  244 – 280

Chapter 7. Inductive Expert Systems

pp.:  280 – 300

Chapter 8. Genetic Algorithms and Neural Networks

pp.:  300 – 330

Chapter 9. Perspectives

pp.:  330 – 359

Index

pp.:  359 – 368

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