Mathematical Principles of Fuzzy Logic

Novák, Vilém; Perfilieva, Irina; Močkoř, Jǐŕı

doi:10.1007/978-1-4615-5217-8

Cited by 903 publications

(571 citation statements)

References 57 publications

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“…Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise. In Fuzzy Logic the degree of truth of a statement can range between 0 and 1 and is not constrained to the two truth values, t and f , as in classic predicate logic [24]. Such degrees can be computed based on various specific membership functions, for example, a trapezoidal function.…”

Section: Encoding Uncertainty In Rifmentioning

confidence: 99%

A Fuzzy Logic-Based Approach to Uncertainty Treatment in the Rule Interchange Format: From Encoding to Extension

Zhao

Boley

Jing

2013

Uncertainty Reasoning for the Semantic Web II

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Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. NRC Publications Record / Notice d'Archives des publications de CNRC:http://nparc.cisti-icist.nrc-cnrc.gc.ca/eng/view/object/?id=d016e61a-7236-421e-8ee1-60d96cc1008d http://nparc.cisti-icist.nrc-cnrc.gc.ca/fra/voir/objet/?id=d016e61a-7236-421e-8ee1-60d96cc1008d Abstract. The Rule Interchange Format (RIF) is a W3C recommendation that allows rules to be exchanged between rule systems. Uncertainty is an intrinsic feature of real world knowledge, hence it is important to take it into account when building logic rule formalisms. However, the set of truth values in the RIF Basic Logic Dialect (RIF-BLD) currently consists of only two values (t and f), although the RIF Framework for Logic Dialects (RIF-FLD) allows for more. In this paper, we first present two techniques of encoding uncertain knowledge and its fuzzy semantics in RIF-BLD presentation syntax. We then propose an extension leading to an Uncertainty Rule Dialect (RIF-URD) to support a direct representation of uncertain knowledge. In addition, rules in Logic Programs (LP) are often used in combination with the other widely-used knowledge representation formalism of the Semantic Web, namely Description Logics (DL), in many application scenarios of the Semantic Web. To prepare DL as well as LP extensions, we present a fuzzy extension to Description Logic Programs (DLP), called Fuzzy DLP, and discuss its mapping to RIF. Such a formalism not only combines DL with LP, as in DLP, but also supports uncertain knowledge representation. 4

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Section: Encoding Uncertainty In Rifmentioning

confidence: 99%

A Fuzzy Logic-Based Approach to Uncertainty Treatment in the Rule Interchange Format: From Encoding to Extension

Zhao

Boley

Jing

2013

Uncertainty Reasoning for the Semantic Web II

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“…In Jahan et al (2011), Li et al (2011 and Cevik and Zaim (2011), the authors proposed geographical and power-based clustering algorithm (GPCA) a heterogeneous-aware clustering protocol in that the SNs are identified by their global positioning system (GPS). In Wang et al (2009) and Novak et al (1999), the authors have presented an analytical approach to determine the optimal number of clusters in dense wireless sensor network (WSN) using cross layer optimisation approach. In Raghuvanshi et al (2010), authors developed routing algorithms based on FCM clustering and subsequently compared optimal and random cluster for different rounds for energy efficient routing algorithms to maximise the life time of WSNs.…”

Section: Introductionmentioning

confidence: 99%

Performance measures of fuzzy C-means algorithm in wireless sensor networks

Kumar

Chaturvedi

2017

IJCAET

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Abstract:The major issues that govern performance of wireless sensor networks (WSNs) are efficient uses of limited resources and appropriate routing decisions of network paths under the severely constrained energy scenario. In this work, to address these issues uses of k-means and fuzzy C-means algorithms are investigated for clusters formation and subsequent selection of cluster heads (CHs). For all these newly formed clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) followed by estimation of Euclidean distances. Depending upon the Euclidean distance measures between the sink node and the estimated energy-centroid (EC) of clusters these clusters are classified into five types. The RES estimation is exercised for all the CHs and sensor nodes (SNs) of the network. Outcomes of simulation results indicate superior performance of fuzzy-c means algorithm compared to k-means algorithm. Further, a case study is presented, wherein the sink is allowed to have some movements in the service area. Here, different quadrant of service area exhibits different pattern of query spatial distribution. The optimal location of sink is sought to support energy efficient operational aspects of the WSNs.

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“…By applying theory of AI, different relative grades can be assigned for most frequently, more frequently, less frequently used parameters considering talking pattern of the subscriber (driver or node). Fuzzy sets [9] are derived from the modified relative grades which are obtained by assigning weightage. Then fuzzy operations [9] are performed on fuzzy sets, results of fuzzy operations are analyzed by setting an invented fuzzy rule or condition.…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy sets [9] are derived from the modified relative grades which are obtained by assigning weightage. Then fuzzy operations [9] are performed on fuzzy sets, results of fuzzy operations are analyzed by setting an invented fuzzy rule or condition. If the results are satisfying the fuzzy rule, all nodes (vehicles) or nodes as well as the network (MSC or PDSN) are authenticated, otherwise not.…”

Section: Introductionmentioning

confidence: 99%