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Extremal Fuzzy Dynamic Systems

Part of the book series: IFSR International Series on Systems Science and Engineering ((IFSR,volume 28))

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Abstract

One of the most important directions of fundamental research in data analysis, uncertain information management, and knowledge engineering is the solution of problems of analysis and synthesis of incomplete and expert information in the setting of many anomalous, extreme, and complex global processes. The prediction and determination of possible scenarios of the evolution of these processes have become highly important. Today, problems of simulation of such processes are intensively studied. These studies, however, show that it is next to impossible to apply deterministic or stochastic methods to such problems because of the complexity of the studied objects or the lack (or insufficiency) of initial objective information. Thus there has arisen a need to develop adequate models in specific spheres of science and engineering (physics, chemistry, ecology, to mention but a few) and to complement them with the knowledge of experts, which is usually not the case with the application of the above-mentioned classical methods. Evaluations and conclusions concerning complex dynamic processes are subjective, since they inevitably reflect the individual knowledge of an expert. Relationships between the objects of a complex dynamic system are frequently of a fuzzy nature (i.e., they are vague, insufficient, uncertain, and weakly structurable). Hence we expect that with time, intelligent modeling will provide new tools for a unified approach to the investigation of processes (problems) in various spheres of human activity. From the standpoint of systems research, the construction of these tools is called intelligent modeling of complex evolutionary weakly structurable systems.

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  1. Department of Computer Sciences, Iv. Javakhishvili Tbilisi St. University, Tbilisi, Georgia

    Gia Sirbiladze

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Sirbiladze, G. (2013). Introduction. In: Extremal Fuzzy Dynamic Systems. IFSR International Series on Systems Science and Engineering, vol 28. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4250-9_1

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