Basic Research – Other Topics

Fusion of Soft and Hard Computing


Combining the Best Techniques of two Worlds

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.

Together with S. J. Ovaska (Espoo/Helsinki, Finland) we introduced a multi-dimensional categorization scheme for fusion techniques that facilitates the discussion of advantages or drawbacks of certain fusion approaches and supports the development of novel fusion techniques and applications. In our lab, we also developed various kinds of fusion techniques, such as

  • combination of conventional clustering techniques with second-order training algorithms for radial basis function neural networks,
  • calibration techniques for self-organizing maps (SOM) applied to classification problems,
  • simultaneous modeling of short-term and long-term trends in time series,
  • combination of evolutionary algorithms and conventional search strategies, and
  • second-order algorithms for the training of a new dynamic neural network paradigm.

Further Information



B. Sick, S. J. Ovaska; Fusion of Soft and Hard Computing: Multi-Dimensional Categorization of Computationally Intelligent Hybrid Systems; in: Neural Computing & Applications; vol. 16, no. 2, pp. 125-137; 2007

S. J. Ovaska, B. Sick; Fusion of Soft Computing and Hard Computing: Applications and Research Opportunities; in G. Yen, D. B. Fogel (Eds.): Computational Intelligence: Principles and Practice; ch. 3, pp. 47-72; IEEE Computational Intelligence Society, Piscataway; 2006 (keynote article of the International Joint Conference on Neural Networks 2006, Vancouver)

B. Sick; Indirect Online Tool Wear Monitoring; in: S. J. Ovaska (Ed.): Computationally Intelligent Hybrid Systems: The Fusion of Soft Computing and Hard Computing; ch. 6, pp. 169-198; Wiley, New York; 2004

C. Bach, S. Bredl, W. Kossa, B. Sick; Calibration of Self-Organizing Maps for Classification Tasks; in: Proceedings of the ”2003 IEEE International Conference on Systems, Man & Cybernetics”; vol. 3, pp. 2877-2882; Washington DC, 2003

C. Gruber, B. Sick; Processing Short-Term and Long-Term Information With a Combination of Hard- and Soft-Computing Techniques; in: Proceedings of the ”2003 IEEE International Conference on Systems, Man & Cybernetics”; vol. 1, pp. 126-133; Washington DC, 2003 (special track on ”Fusion of Soft Computing and Hard Computing”)

C. Gruber, B. Sick; Fast and Efficient Second-Order Training of the Dynamic Neural Network Paradigm; in: Proceedings of the ”IEEE-INNS International Joint Conference on Neural Networks (IJCNN 2003)”; vol. 4, pp. 2482-2487; Portland, 2003

O. Buchtala, P. Neumann, B. Sick; A Strategy for an Efficient Training of Radial Basis Function Networks for Classification Applications; in: Proceedings of the ”IEEE-INNS International Joint Conference on Neural Networks (IJCNN 2003)”; vol. 2, pp. 1025-1030; Portland, 2003

B. Sick; Fusion of Hard and Soft Computing Techniques in Indirect, Online Tool Wear Monitoring; in: IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews, Special Issue on Fusion of Soft Computing and Hard Computing in Industrial Applications; vol. 32, no. 2, pp. 80-91; 2002

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Collaborative Knowledge Discovery


“The Wisdom of Crowds”

“The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations” is a book written by James Surowiecki about the aggregation of information in groups, resulting in better decisions. We aim at adopting this idea in the field of Knowledge Discovery (KD) and Data Mining (DM).

Experts have important qualitative knowledge about interrelations between more or less abstract concepts in an application area. However, the knowledge of a single expert is typically quite uncertain (e.g., incomplete or imprecise). By fusing the knowledge of several experts it would be possible to obtain more certain and, therefore, more valuable knowledge. Conventional systems for KD and DM have the ability to extract valid rules from huge data sets. These rules describe dependencies between attributes and classes in a quantitative way, for instance. By fusing this kind of knowledge with the combined, qualitative knowledge of several experts it would be possible to obtain more comprehensive knowledge about an application area.

Example for the utilization of a CKD system with fused application knowledge.

We proposed a concept for a new KD & DM technique based on Computational Intelligence: Collaborative Knowledge Discovery (CKD). This technique combines the uncertain knowledge of several experts and the combined human knowledge is again fused with automatically extracted, well interpretable knowledge of a conventional KD system. Thus, a CKD system not only acquires more comprehensive knowledge, but also experience (knowledge about knowledge), meaning that it is able to explain automatically extracted rules to the human experts and to assess the interestingness (e.g., novelty or utility) of these rules. A CKD system will comprise self-awareness mechanisms (it must know what it knows) as well as environment-awareness mechanisms (it must know what human experts know or what they want to now). In order to reduce the effort for knowledge acquisition, a CKD system must learn proactively. There are many application areas for such CKD systems, e.g., in the field of technical data mining (quality control, process monitoring, etc.).

Further Information



D. Andrade, T. Horeis, B. Sick; Knowledge Fusion Using Dempster-Shafer Theory and the Imprecise Dirichlet Model; in: Proceedings of the ”2008 IEEE Conference on Soft Computing in Industrial Applications (SMCia/08)”; pp. 142-148; Muroran, 2008

T. Horeis, B. Sick; Collaborative Knowledge Discovery & Data Mining: From Knowledge to Experience; in: Proceedings of the ”2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007)”; pp. 421-428; Honolulu, 2007

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