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Soft-Computing Methods

In: Mathematical and Computational Modeling and Simulation

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  • Dietmar P. F. Moeller

    (California State University, Chico College of Engineering, Computer Science and Technology O’Connel Technology Center
    University of Hamburg, Faculty of Computer Science)

Abstract

The formalizations of modeling are only useful if they succeed in seizing the essential features of the dynamic system under test. They permit extrapolations that allows one to generalize, often correctly, from past experience to future events from which we can learn how the dynamic system can be manipulated for ones purposes, which is a kind of uncertainty. In our world, which is more or less precisely understandable or predictable, we are more conscious of uncertainty. This uncertainty appears in the form of imprecision, vagueness, and ill-defined, ill-separable, and doubtful data. Using nonprecise information, called soft-information, needs a specific form of computation, called soft-computing. There are four main classes of methods that form soft-computing: Neural networks Fuzzy logic Genetic algorithms Probabilistic reasoning Although each of these classes of methods can be used to resolve certain types of applications, they are in fact complementary to each other, and in many cases it can be better to employ them in combination rather than exclusively.

Suggested Citation

  • Dietmar P. F. Moeller, 2004. "Soft-Computing Methods," Springer Books, in: Mathematical and Computational Modeling and Simulation, chapter 6, pages 311-338, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-18709-4_6
    DOI: 10.1007/978-3-642-18709-4_6
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