Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We describe the basic properties of a norm and introduce the Minkowski norm. We then show that the OWA aggregation operator can be used to provide norms. To enable this we require that the OWA weights satisfy the buoyancy property, <formula formulatype="inline"><tex Notation="TeX">$w_{j} \geq w_{k}$</tex></formula> for <formula formulatype="inline"><tex Notation="TeX"> $j$</tex></formula> < <formula formulatype="inline"><tex Notation="TeX">$k$</tex></formula>. We consider a number of different classes of OWA norms. It is shown that the functional generation of the weights of an OWA norm requires the weight generating function have a non-positive second derivative. We discuss the use of the generalized OWA operator to provide norms. Finally we describe the use of OWA operators to induce similarity measures. </para>
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
