Fuzzy-Set Based Logics — an History-Oriented Presentation of their Main Developments

Dubois, Didier; Esteva, Francesc; Godo, Lluı́s; Prade, Henri

doi:10.1016/s1874-5857(07)80009-4

Cited by 50 publications

(14 citation statements)

References 230 publications

(224 reference statements)

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“…Water membership values were used for this purpose. The certainty of pixel k to belong to any class was assessed using possibility and necessity measures [44][45][46][47]. If C is a subset of a universe of discourse U and x P C, then π x puq is the degree of possibility that x takes value u.…”

Section: Uncertainty Estimationmentioning

confidence: 99%

Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia

et al. 2016

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Abstract:This study presents an unsupervised fuzzy c-means classification (FCM) to observe the shoreline positions. We combined crisp and fuzzy methods for change detection. We addressed two perspectives of uncertainty: (1) uncertainty that is inherent to shoreline positions as observed from remote sensing images due to its continuous variation over time; and (2) the uncertainty of the change results propagating from object extraction and implementation of shoreline change detection method. Unsupervised FCM achieved the highest kappa (κ) value when threshold (t) was set at 0. 2013-2014 (65 ha). Urban development in flood-prone areas resulted in an increase of flood hazards including inundation and erosion leading to the changes of shoreline position. The proposed methods provided an effective way to present shoreline as a line and as a margin with fuzzy boundary and its associated change uncertainty. Shoreline mapping and monitoring is crucial to understand the spatial distribution of coastal inundation including its trend.

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Section: Uncertainty Estimationmentioning

confidence: 99%

Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia

et al. 2016

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“…The conditional statement (or proposition) contains a premise, the if-part, and a conclusion, the then-part [12,14]. The knowledge included in a fuzzy control system is made up of a group of several rules of the form "if X is A then Y is B", or, more generally, "if X1 is A1 and ...and Xn is An then Y is B", where A, An, B are fuzzy sets [15]. The knowledge base, which comprehends general knowledge concerning a problem domain, joins antecedents with consequences or premises with conclusions [16] (see Figure 1).…”

Section: Fuzzy Inference System: a Theoretical Reviewmentioning

confidence: 99%

A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

Cavallaro

2015

Sustainability

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Abstract:One aspect of the use of biomass for energy purposes which remains controversial concerns their full environmental sustainability. Considering the crucial importance of this problem, numerous authors have carried out evaluations of the environmental impact of the various types of biomass by means of several approaches. Although some of these methods are excellent environmental evaluation tools, they are unfortunately unable to manage uncertain input data. Instead, fuzzy-set based methods have proven to be able to deal with uncertainty in environmental topics. The original contributions proposed by fuzzy logic relate, on the one hand, to the representation of uncertain and vague information, and, on the other, to handling such information using fuzzy rules. A fuzzy inference system (FIS) constitutes the practice of framing mapping from the input to an output using fuzzy logic. In this paper, we propose an application of Takagi-Sugeno fuzzy inference modelling to build a synthetic index to assess the sustainability of production of the biomass for energy purposes.

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“…Indeed, formalisms that cope with graded truth (fuzzy logics) radically departs from the formalisms for uncertainty reasoning [45,38]. Giving up the bivalence principle and adopting a truth scale with intermediate degrees between 0 (false) and 1 (true) (usually the unit real interval [0, 1]) lead to a number of many-valued truth-functional systems with connectives extending the classical ones.…”

Section: Logics Of Graded Truthmentioning

confidence: 99%

Weighted logics for artificial intelligence – an introductory discussion

Dubois

Godo

Prade

2014

International Journal of Approximate Reasoning

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Fuzzy-Set Based Logics — an History-Oriented Presentation of their Main Developments

Cited by 50 publications

References 230 publications

Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia

Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia

A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

Weighted logics for artificial intelligence – an introductory discussion

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