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Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems

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Abstract

The concept of fusion of soft computing and hard computing has rapidly gained importance over the last few years. Soft computing is known as a complementary set of techniques such as neural networks, fuzzy systems, or evolutionary computation which are able to deal with uncertainty, partial truth, and imprecision. Hard computing, i.e., the huge set of traditional techniques, is usually seen as the antipode of soft computing. Fusion of soft and hard computing techniques aims at exploiting the particular advantages of both realms. This article introduces a multi-dimensional categorization scheme for fusion techniques and applies it by analyzing several fusion techniques where the soft computing part is realized by a neural network. The categorization scheme facilitates the discussion of advantages or drawbacks of certain fusion approaches, thus supporting the development of novel fusion techniques and applications.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Passau, 94030, Passau, Germany

    Bernhard Sick

  2. Institute of Intelligent Power Electronics, Helsinki University of Technology, 02150, Espoo, Finland

    Seppo J. Ovaska

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  1. Bernhard Sick
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  2. Seppo J. Ovaska
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Correspondence to Bernhard Sick.

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Sick, B., Ovaska, S.J. Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems. Neural Comput & Applic 16, 125–137 (2007). https://doi.org/10.1007/s00521-006-0045-y

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