Abstract
This paper focuses on a novel methodological framework for converting a Fuzzy Cognitive Map into a network of rule-based Fuzzy Inference Systems. Furthermore, it allows to obtain a crisp value representing an arbitrary parameter of the complex system’s model. This way the system provides a quantitative answer without employing an exact mathematical model. This paper also outlines a first possible application area: the valuation of investments in high-technology ventures. A field in which usually conventional quantitative and retrospective measures usually do not deliver satisfying results due to the complexity of future-oriented risk prognosis and the lack of quantitative data.
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Krüger, L. (2010). K2F – A Novel Framework for Converting Fuzzy Cognitive Maps into Rule-Based Fuzzy Inference Systems. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_17
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DOI: https://doi.org/10.1007/978-3-642-13208-7_17
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