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Soft Computing: Fuzzy Logic, Neural Networks, and Genetic Algorithms

  • Oliver Grillmeyer
Chapter
Part of the Undergraduate Texts in Computer Science book series (UTCS)

Abstract

Soft computing is a relatively new field within computer science. It is a conglomeration of fuzzy logic, neural networks, and probabilistic reasoning. Probabilistic reasoning is further divided into belief networks, genetic algorithms, and chaos theory. What all of these subfields share is an adherence to nonexact computation. Up until now, we have been using formal Boolean logic, which says that something is either true or false, yes or no, black or white. There are no shades of gray with this type of logic.

Keywords

Neural Network Genetic Algorithm Hide Layer Fuzzy Logic Fuzzy System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Additional Reading

  1. Cox, E.D. (1995). Fuzzy Logic for Business and Industry, Charles River Media Inc., Rockland, MA.Google Scholar
  2. Kosko, B. (1993). Fuzzy Thinking: The New Science of Fuzzy Logic, Hyperion, New York, NY.Google Scholar
  3. Von Altrock, C. (1995). Fuzzy Logic and NeuroFuzzy Applications Explained, Prentice Hall PTR, Englewood Cliffs, N.J.Google Scholar

Neural Networks

  1. Kosko, B. (1992). Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall, Englewood Cliffs, NJ.MATHGoogle Scholar
  2. McClelland, J.L., Rumelhart, D.E., and the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 2: Psychological and Biological Models, MIT Press, Cambridge, MA.Google Scholar
  3. Rumelhart, D.E., McClelland, J.L., and the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, MIT Press, Cambridge, MA.Google Scholar

Genetic Algorithms

  1. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.MATHGoogle Scholar
  2. Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA.MATHGoogle Scholar
  3. Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs, Third revision and extended edition, Springer-Verlag, Berlin, Germany.MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Oliver Grillmeyer
    • 1
  1. 1.Department of Computer ScienceUniversity of California at BerkeleyBerkeleyUSA

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