Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
1. Introduction. Automation and intelligent software. Expert systems. Neural networks and genetic algorithms. Reader's guide. Concepts. Conclusions. 2. Knowledge-based Systems in Chemical Analysis (P. Schoenmakers). Computers in analytical chemistry. Sample preparation. Method selection. Method development. Instrument control and error diagnosis. Data handling and calibration. Data interpretation. Validation. Laboratory management. Concluding remarks. Concepts. Conclusions. Bibliography. 3. Developing Expert Systems (H. van Leeuwen). Introduction. Prerequisites. Knowledge acquisition. Knowledge engineering. Inferencing. Explanation facilities. The integration of separate systems. Expert-system testing validation and evaluation. Concepts. Conclusions. Bibliography. 4. Expert-System-Development Tools (L. Buydens, H. van Leeuwen, R. Wehrens). Tools for implementing expert systems. Tool selection. Knowledge-acquisition tools. Concepts. Conclusions. Bibliography. 5. Validation and Evaluation of Expert Systems for HPLC Method Development - Case Studies (F. Maris, R. Hindriks). Introduction. Case study I: Expert systems for method selection and selectivity optimization. Case study II: System-optimization expert system. Case study III: Expert system for repeatability testing, applied for trouble-shooting in HPLC. Case study IV: Ruggedness-testing expert system. General comments on the evaluations. Concepts. Conclusions. Bibliography. 6. Self-adaptive Expert Systems (R. Wehrens). Introduction - maintaining expert systems. Self-adaptive expert systems: Methods and approaches. The refinement approach of SEEK. Examples from analytical chemistry. Concluding remarks. Concepts. Conclusions. Bibliography. 7. Inductive Expert Systems (R. Wehrens, L. Buydens). Introduction. Inductive classification by ID3. Applications of ID3 in analytical chemistry. Concluding remarks. Concepts. Conclusions. Bibliography. 8. Genetic Algorithms and Neural Networks (G. Kateman). Introduction. Genetic algorithms. Artificial neural networks. Concepts. Conclusions. Bibliography. 9. Perspectives. Limitations of Intelligent Software. Dealing with intelligent software. Potential of intelligent software. Index.
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.
- No. of pages:
- © Elsevier Science 1993
- 3rd September 1993
- Elsevier Science
- eBook ISBN:
@qu:The case studies make this book a particularly good introduction to expert systems.
@qu:...would be most useful for practicing analytical chemists in industry or elsewhere, chemometricians, graduate students and software engineers.
@source:Journal of Chemical Information and Computer Sciences
@qu:...a very valuable book offering both a good introduction and a critical view of today's status of expert systems.
@source:Monitor/Chemometrics and Intelligent Laboratory Systems
Catholic University of Nijmegen, Nijmegen, The Netherlands
Koninklijke/Shell Laboratorium Amsterdam, Amsterdam, The Netherlands
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.