| Description |
1 online resource (xxi, 429 pages) : illustrations |
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data file |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Ch. 1. Introduction -- ch. 2. Rule-based systems -- ch. 3. Handling uncertainty : probability and fuzzy logic -- ch. 4. Agents, objects, and frames -- ch. 5. Symbolic learning -- ch. 6. Single-candidate optimization algorithms -- ch. 7. Genetic algorithms for optimization -- ch. 8. Neural networks -- ch. 9. Hybrid systems -- ch. 10. Artificial intelligence programming languages -- ch. 11. Systems for interpretation and diagnosis -- ch. 12. Systems for design and selection -- ch. 13. Systems for planning -- ch. 14. Systems for control -- ch. 15. The future of intelligent systems. |
| Summary |
The third edition of this best-selling reference examines the principles of artificial intelligence and their application in engineering and science as well as techniques for developing intelligent systems to solve problems. The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids.Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself.The first part of the book describes key techniques of artificial intelligence-including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control.The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance.Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit |
| Language |
English. |
| Subject |
Expert systems (Computer science)
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Computer-aided engineering.
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Systèmes experts (Informatique) |
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Ingénierie assistée par ordinateur. |
|
computer-aided engineering. |
|
Computer-aided engineering |
|
Expert systems (Computer science) |
| Other Form: |
Print version: Hopgood, Adrian A. Intelligent systems for engineers and scientists. 3rd ed. Boca Raton, FL : CRC Press, ©2012 9781439821206 (DLC) 2011038962 (OCoLC)701015778 |
| ISBN |
9781439865965 (electronic bk.) |
|
1439865965 (electronic bk.) |
|
0429064101 |
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9780429064104 |
|
1280122528 |
|
9781280122521 |
|
9786613526380 |
|
661352638X |
| Standard No. |
10.1201/b11287 doi |
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