This chapter discusses some specific tools that can be used to build triangular norms based on a finite number of (possibly noisy) observations. Such problem arises in applications, when observed data (e.g., decision patterns of experts) need to be modelled with a special class of functions, such as triangular norms. We show how this problem can be transformed into a constrained regression problem, and then efficiently solved. We also discuss related operators: uninorms, nullnorms and associative copulas.
Field of Research
010107 Mathematical Logic, Set Theory, Lattices and Universal Algebra
Socio Economic Objective
970101 Expanding Knowledge in the Mathematical Sciences
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact firstname.lastname@example.org.