21 research outputs found
Eliciting Expert Knowledge for Fuzzy Evaluation of Agricultural Production Systems
Public concern nowadays is an important frame of reference for thedevelopment of agricultural production systems. The development ofsuch systems, therefore, involves both society level and productionsystem level. Following Zadeh's principle of incompatibility,information obtained at production system level is interpreted atsociety level in linguistic terms. Fuzzy models promise to be avaluable tool as they link measurable information to linguisticinterpretation using membership functions. The objective of this paperis to outline a procedure which deals with criticism regarding theinherent subjectivity in the construction of membership functions whenusing expert knowledge. The procedure guarantees the selection ofappropriate expert knowledge, and provides a guideline supporting theselection of methods to elicit expert knowledge and constructmembership functions. Also on the basis of the results in anillustrative example, it is concluded that the procedure outlined inthis paper suitably deals with criticism regarding membershipfunctions and, therefore, enables a practical implementation of fuzzyevaluation of agricultural production systems. Current researchimplements the procedure to build a fuzzy model which evaluates eggproduction systems in relation to public concern about the welfare oflaying hens.evaluation;subjectivity;expert knowledge;fuzzy models;knowledge elicitation
Assessment of Sustainable Development
The objective of this paper is to introduce fuzzy set theory and develop fuzzy mathematical models to assess sustainable development based on context-dependent economic, ecological, and societal sustainability indicators. Membership functions are at the core of fuzzy models, and define the degree to which indicators contribute to development. Although a decision-making process regarding sustainable development is subjective, fuzzy set theory links human expectations about development, expressed in linguistic propositions, to numerical data, expressed in measurements of sustainability indicators. In the future, practical implementation of such models will be based on elicitation of expert knowledge to construct a membership function. The fuzzy models developed in this paper provide a novel approach to support decisions regarding sustainable development.agriculture;assessment;fuzzy set theory;sustainable development
Assessment of Sustainable Development
The objective of this paper is to introduce fuzzy set theory and develop fuzzy mathematical models to assess sustainable development based on context-dependent economic, ecological, and societal sustainability indicators. Membership functions are at the core of fuzzy models, and define the degree to which indicators contribute to development. Although a decision-making process regarding sustainable development is subjective, fuzzy set theory links human expectations about development, expressed in linguistic propositions, to numerical data, expressed in measurements of sustainability indicators. In the future, practical implementation of such models will be based on elicitation of expert knowledge to construct a membership function. The fuzzy models developed in this paper provide a novel approach to support decisions regarding sustainable development
Eliciting Expert Knowledge for Fuzzy Evaluation of Agricultural Production Systems
Public concern nowadays is an important frame of reference for the
development of agricultural production systems. The development of
such systems, therefore, involves both society level and production
system level. Following Zadeh's principle of incompatibility,
information obtained at production system level is interpreted at
society level in linguistic terms. Fuzzy models promise to be a
valuable tool as they link measurable information to linguistic
interpretation using membership functions. The objective of this paper
is to outline a procedure which deals with criticism regarding the
inherent subjectivity in the construction of membership functions when
using expert knowledge. The procedure guarantees the selection of
appropriate expert knowledge, and provides a guideline supporting the
selection of methods to elicit expert knowledge and construct
membership functions. Also on the basis of the results in an
illustrative example, it is concluded that the procedure outlined in
this paper suitably deals with criticism regarding membership
functions and, therefore, enables a practical implementation of fuzzy
evaluation of agricultural production systems. Current research
implements the procedure to build a fuzzy model which evaluates egg
production systems in relation to public concern about the welfare of
laying hens