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خانه مقالات-Article مقالات کامپیوتر-Computer Articles سیستم فازی-Fuzzy System Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models
Assessment of uncertainties in expert knowledge, illustrated in fuzzy[taliem.ir]

Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models

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J.A.E.B. Janssen a,∗, M.S. Krol a, R.M.J. Schielen a,b, A.Y. Hoekstra a, J.-L. de Kok a,c
a University of Twente, Water Management and Engineering Group, Enschede, The Netherlands
b Ministry of Transport, Public Works and Water Management, Waterdienst, Lelystad, The Netherlands
c VITO, Flemish Institute for Technological Research, Centre for Integrated Environmental Studies, Boeretang 200, B-2400 Mol, Belgium

The coherence between different aspects in the environmental system leads to a demand for comprehensive models of this system to explore  the effects of different management alternatives. Fuzzy logic has been suggested as a means to extend the application domain of  environmental  modelling from physical relations to expert knowledge. In such applications the expert describes the system in terms of fuzzy variables and inference rules. The result of the fuzzy reasoning process is a numerical output value. In such a model, as in any other, the model  context, structure, technical aspects, parameters and inputs may contribute uncertainties to the model output. Analysis of these contributions  in a simplified model for agriculture suitability shows how important information about the accuracy of the expert knowledge in relation to the  other uncertainties can be provided. A method for the extensive assessment of uncertainties in  compositional fuzzy rule-based models is  proposed, combining the evaluation of model structure, input and parameter uncertainties. In an example model, each of these three appear to  have the potential to dominate aggregated uncertainty, supporting the relevance of an ample uncertainty approach.

توضیحات محصول

ABSTRACT

The coherence between different aspects in the environmental system leads to a demand for comprehensive models of this system to explore  the effects of different management alternatives. Fuzzy logic has been suggested as a means to extend the application domain of  environmental  modelling from physical relations to expert knowledge. In such applications the expert describes the system in terms of fuzzy variables and inference rules. The result of the fuzzy reasoning process is a numerical output value. In such a model, as in any other, the model  context, structure, technical aspects, parameters and inputs may contribute uncertainties to the model output. Analysis of these contributions  in a simplified model for agriculture suitability shows how important information about the accuracy of the expert knowledge in relation to the  other uncertainties can be provided. A method for the extensive assessment of uncertainties in compositional fuzzy rule-based models is  proposed, combining the evaluation of model structure, input and parameter uncertainties. In an example model, each of these three appear to  have the potential to dominate aggregated uncertainty, supporting the relevance of an ample uncertainty approach.

INTRODUCTION

In densely populated delta areas, water management requires balancing of many different interests and user functions. Because of the many  different actors, and interaction with the physical environment, governed by many different physical processes, and the need for knowledge  from many different areas, the decision making process becomes very complex. To support the decision and policy making process, different  tools are utilized. Among these are software tools, where collected data and analytical models serve, for instance, to explore different policy  options, analyse real time events, or predict future states of the system configuration. The fact that not all desired information can be described  in physical terms may restrict the application of such models. Sometimes experts may be able to provide valuable additional information. In such  cases the application of fuzzy rule-based models can be an option (Adriaenssens et al., 2004; Ascough et al., 2008). As in any other environmental modelling approach, it is important to address the uncertainty in the model’s output. This uncertainty assessment is the result of  the conceptualization of expert knowledge in a fuzzy rule-based model.

Year: 2010

Publisher : ELSEVIER

By : J.A.E.B. Janssen  , M.S. Krol  , R.M.J. Schielen  , A.Y. Hoekstra a, J.-L. de Kok

File Information: English Language/ 7 Page / size: 452 KB

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سال : 2010

ناشر : ELSEVIER

کاری از :  J.A.E.B. Janssen ,  M.S. Krol , R.M.J. Schielen , A.Y. Hoekstra , J.-L. de Kok 

اطلاعات فایل : زبان انگلیسی / 7صفحه / حجم : KB 452

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