Authors S.M. Kovalev, A.N. Shabelnikov
Month, Year 03, 2017 @en
Index UDC 007:519.816
Abstract This paper proposes a new primary information processing methodology in integrated dynamical process control systems, which is based on the methods for hierarchical intelligent preprocessing. Proposed methodology allows not only to eliminate various noises and distortions in primary data, but also to realize their adequate interpretation for the following decision making. As the basis of the hierarchical preprocessing, knowledge models are used. Lower hierarchical levels use process knowledge, which generate primary information and allow organizing an effective clearing of primary information from noises and distortions, higher ones use functional knowledge about technological process and control aims, which allow to increase efficacy of decision making. As a basic decision making engine, hybrid fuzzy stochastic scheme is used on lower level. The scheme models of dynamic character of information together with fuzzy and stochastic uncertainties are used. Upper level maps obtained results into pragmatic scale of aggregated estimates, which is presented by a small number of linguistic values, which provides the possibility of decision making with acceptable level of uncertainty. The key feature of proposed hybrid fuzzy inference scheme is integration of both stochastic information about distributions of measured variables and fuzzy information about membership functions behavior. Top level of hybrid system uses granular logical models. Decision making algorithm is based on granular logical semantics. Application of logic-algebraic approach is considered for interpretation and analysis of linguistic data.

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Keywords Intelligent systems; hierarchical intelligent preprocessing; measurement uncertainty; multi-valued logics; hybrid fuzzy stochastic scheme.
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