Granular Computing and Intelligent Systems: Design with by Vladik Kreinovich (auth.), Witold Pedrycz, Shyi-Ming Chen

By Vladik Kreinovich (auth.), Witold Pedrycz, Shyi-Ming Chen (eds.)

Information granules are primary conceptual entities facilitating conception of complicated phenomena and contributing to the enhancement of human centricity in clever structures. The formal frameworks of data granules and knowledge granulation contain fuzzy units, period research, likelihood, tough units, and shadowed units, to call just a couple of representatives. between present advancements of Granular Computing, fascinating ideas main issue details granules of upper order and of upper variety. the better order info granularity is worried with a good formation of knowledge granules over the distance being initially built through details granules of reduce order. This build is at once linked to the idea that of hierarchy of platforms composed of successive processing layers characterised through the expanding degrees of abstraction. this concept of layered, hierarchical cognizance of versions of advanced structures has received an important point of visibility in fuzzy modeling with the well-established thought of hierarchical fuzzy types the place one strives to accomplish a legitimate tradeoff among accuracy and a degree of element captured via the version and its point of interpretability. larger style details granules emerge whilst the data granules themselves can't be absolutely characterised in a in simple terms numerical model yet as a substitute it turns into handy to use their consciousness within the type of different kinds of info granules equivalent to type-2 fuzzy units, interval-valued fuzzy units, or probabilistic fuzzy units. better order and better form of info granules represent the focal point of the reports on Granular Computing offered during this examine. The booklet elaborates on sound methodologies of Granular Computing, algorithmic ambitions and an array of various purposes and case stories in environmental reviews, choice expense forecasting, and gear engineering.

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Additional info for Granular Computing and Intelligent Systems: Design with Information Granules of Higher Order and Higher Type

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274–282. : Granular computing: past, present and future. In: Proceedings of 2008 IEEE International Conference on Granular Computing, Hangzhou, China, pp. : A unified framework of granular computing. , Kreinovich, V. ) Handbook of Granular Computing, pp. 401–410. : The rise of granular computing. : Interpreting concept learning in cognitive informatics and granular computing. : Integrative levels of granularity. , Pedrycz, W. ) HumanCentric Information Processing. SCI, vol. 182, pp. 31–47. : Human-inspired granular computing.

Ye and Tsotsos (1998) use the notion of knowledge granularity to study an agent’s action selection process. Mani (1998) applies granularity to solve the problem of underspecification of meaning in natural languages. McCalla et al. (1992) introduce the concept of granularity hierarchies and apply it in intelligent tutoring systems. By interpreting granulation as abstraction, it is possible to adopt many features of abstraction to granular computing. At the same time, granulation offers a new view of abstraction, which has been less considered in the study of abstraction.

Bazan 2008, Bazan et al. 2006)). The chapter is organized as follows. In Section 2 we discuss basic notions for our approach. In Section 3 we present a generalization of the approximation space definition from (Skowron and Stepaniuk 1994, Skowron and Stepaniuk 1996, Stepaniuk 2008). 2 we present new rough set approach to function approximation. 1 Attributes, Signatures of Objects and Two Semantics In (Pawlak and Skowron 2007) any attribute a is defined as a function from the universe of objects U into the set of attribute values Va .

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