Xu, L.D.

Elsevier - Publisher Connector 

Fuzzy multicriteria programming in economic systems analysis 

Abstracts I 1 ! 
Fuzzy Multicriteria Programming in Economic Systems Analysis 
L. D. Xu 
Information Systems, University of Wisconsin, La Crosse, 
Wisconsin 54601 
In this paper, four main aspects of both the inquiring and operational systems of economic 
systems analysis are explored: (1) A new philosophical paradigm as the foundation of 
general methodology in place of Kantian-Newtonian nquiring systems is proposed. (2) A 
new problem formulation space--a multidimensional, synergetic, and autopoietic 
model--is proposed. (3) The new philosophical paradigm ischaracterized as a Singerian 
inquiring system, and Marglin's multiobjective analysis is replaced by Singerian 
multiobjective analysis. (4) Fuzzy set theory and fuzzy Markov theory are introduced for 
multiobjective analysis. 
An interactive and iterative fuzzy programming method is proposed for solving a 
quasi-optimization problem under constraints involving a multiple objective function. 
Comparing this with the adapted gradient search method, surrogate worth trade-off 
method, and the Zionts-Wallenius method, the approximate preference structure is 
emphasized. 
The paper discusses how to specify a set of nondominated solutions and approach a 
nondominated xtreme point from the perspective of the decision maker. One difference 
between the surrogate worth trade-off method and the proposed method is that the former 
indicates the marginal rates of substitution with the limitation of pair trade-off, while the 
latter shows an approximate preference structure for generating a new ideal point. The 
trade-off of pairs is substituted by multidimensional evaluations without assuming that 
certain conditions remain unchanged. 
Application of Possibility Theory to Learning in Knowledge-Based 
Systems with an Imperfect Teacher 
Maria Zemankova 
Computer Science Department, University of Tennessee, 107 Ayres 
Hall, Knoxville, Tennessee 37996-1301 
The goal of the learning process is to build a knowledge base consisting of a set of 
concepts defined over an object set described by its attributes. The object set is be 
grouped into equivalence lasses using similarity relations defined on attribute domains. 
The learning algorithm is based on finding a possibility function value for each 
equivalence lass or the degree to which this class satisfies the concept being learned. 
This function is translated into an optimized escription of the concept hat can be easily 
read by users. During the learning process the teacher provides positive and negative 
examples, and the system computes the possibility function values for the boundary 
region that can be corrected by the teacher if necessary. The teacher may be 
"imperfect' '--he or she does not have to know the concepts exactly--and the system will 


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Fuzzy multicriteria programming in economic systems analysis

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