Generalized Choquet fuzzy integral fusion

Auephanwiriyakul, Sansanee; Keller, James M.; Gader, Paul D.

doi:10.1016/s1566-2535(01)00054-9

Cited by 103 publications

(73 citation statements)

References 18 publications

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“…Examples of rare alternative approaches are [10] (neural networks) and [11] (fuzzy fusion of classifiers). The statistical approaches lead to good results for a particular scenario, but they ignore or just briefly mention that, once we look for more general solutions, several important problems have to be faced in this domain of application [12].…”

Section: About Close-range Detectionmentioning

confidence: 99%

Data Fusion for Close‐Range Detection

Milisavljević¹

2017

Mine Action - The Research Experience of the Royal Military Academy of Belgium

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Two approaches for combining humanitarian mine detection sensors are described in parallel, one based on belief functions and the other one based on possibility theory. In a first step, different measures are extracted from the sensor data. After that, based on prior information, mass functions and possibility distributions are derived. The combination of possibility degrees, as well as of masses, is performed in two steps. The first one applies to all measures derived from one sensor. The second one combines results obtained in the first step for all sensors used. Combination operators are chosen to account for different characteristics of the sensors. Comparison of the combination equations of the two approaches is performed as well. Furthermore, selection of the decision rules is discussed for both approaches. These approaches are illustrated on a set of real mines and non-dangerous objects and using three sensors: an infrared camera, an imaging metal detector and a ground-penetrating radar.

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Section: About Close-range Detectionmentioning

confidence: 99%

Data Fusion for Close‐Range Detection

Milisavljević¹

2017

Mine Action - The Research Experience of the Royal Military Academy of Belgium

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“…A Choquet integral is an extension of the standard fuzzy integral [33]. The Choquet and Sugeno integrals are used as aggregation operators.…”

Section: Generalised Choquet Integral (Gci)mentioning

confidence: 99%

“…The importance of a criterion and interactions between criteria are represented in a Choquet integral. The generalised Choquet integral proposed in Auephanwiriyahul et al [33], in which measurable evidence is represented in terms of intervals, whereas fuzzy measures are real numbers, is an extension of the standard Choquet integral. In the generalised version of the Choquet integral, measurements are done by using intervals instead of real numbers [31,35].…”

Section: Generalised Choquet Integral (Gci)mentioning

confidence: 99%

A Two-Phased Multi-Criteria Decision-Making Approach for Selecting the Best Smartphone

Yıldız

Ergul

2015

SAJIE

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In the last 20 years, rapid and significant developments have occurred in communication and information technologies. In parallel with these developments, the importance of smartphones has increased. In addition, many smartphone manufacturers have launched and continue to launch a number of new models with many features. People who want to buy a new smartphone have difficulties selecting the best smartphone among the numerous models available on the technology markets. Therefore, smartphone selection has become a complex multi-criteria decision-making (MCDM) problem for people. Hence, decision-making processes will be facilitated by using MCDM methods, and these will provide the most appropriate decision. In this paper, the best smartphone among the 28 alternatives determined by the person who will buy them are selected by using three main criteria and 17 sub-criteria with the help of a two-phased MCDM approach. In the first phase, 28 smartphone alternatives are ranked using the analytic network process (ANP). In the second phase, a model that includes the best four alternatives of ANP is created. Afterwards, the best smartphone is selected using the generalised Choquet integral (GCI) method according to this model. Finally, the findings and the results are given. OPSOMMINGGedurende die afgelope twintig jaar het daar vinnige en noemenswaardige ontwikkeling in die kommunikasie en informasie tegnologie geskied. In parallel hiermee het die belangrikheid van slimtelefone toegeneem. Daarmee saam stel slimtelefoon-vervaardigers gereeld nuwe modelle met nuwe funksies vry. Dit is dus moeilik vir 'n potensiële kliënt om die beste seleksie uit die groot verskeidenheid tot hul beskikking te maak. Slimtelefoonseleksie is 'n ingewikkelde multi-kriteria besluitnemingsprobleem. Die besluitnemingsproses word dus gefasiliteer deur gebruik te maak van multi-kriteria besluitnemingsmetodes. Hierdie artikel bepaal die beste slimtelefoon vanuit agt-en-twintig alternatiewe deur gebruik te maak van drie hoof kriteria en sewentien subkriteria met die hulp van 'n twee-ledige multi-kriteria besluitnemingsbenadering. Eerstens word die agt-entwintig alternatiewe met behulp van die analitiese netwerk proses gerangskik en daaruit word 'n model, wat uit die vier beste alternatiewe bestaan, geskep. Laastens word die beste slimtelefoon gekies deur die veralgemeende Choquet integraal metode op die model toe te pas.

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“…A reliable way of obtaining an estimate of the dominant fundamental frequency is to use the cepstrum [1], which is a Fourier analysis of the logarithmic magnitude spectrum of the signal. If the log amplitude spectrum contains many regularly spaced harmonics, then the Fourier analysis of this spectrum will show a peak corresponding to the spacing between the harmonics, i.e.…”

Section: Frequency Domain Fo Estimation: Cepstrummentioning

confidence: 99%

“…The autocorrelation function was then calculated for this section of the signal, and the fundamental frequency was estimated by looking for a peak in the delay interval corresponding to the typical range of seismic frequencies i.e. [1,20] Hz [2]. This method produces peaks at sub-harmonics as well as at the fundamental frequency, and it is difficult to determine which peak corresponds to the fundamental frequency.…”

Section: Time Domain F Estimation: Autocorrelationmentioning

confidence: 99%

Fuzzy Logic for Unattended Ground Sensor Fusion

Bharadwaj¹,

Mendel²

2006

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Generalized Choquet fuzzy integral fusion

Cited by 103 publications

References 18 publications

Data Fusion for Close‐Range Detection

Data Fusion for Close‐Range Detection

A Two-Phased Multi-Criteria Decision-Making Approach for Selecting the Best Smartphone

Fuzzy Logic for Unattended Ground Sensor Fusion

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