A neural network approach to modeling fuzzy preference relations for multiple criteria decision making

This paper examines the framework of preference structures for the general treatment of information. First some fuzzy preference structures are introduced and then, by means of ambiguity and coherence measures, certain attributes of information can be explicitly identified and studied. This investigation is also relevant for a general discussion on fuzzy preference semantics.

The ordinal consistency of a fuzzy preference relation

Preference modelling is a fundamental part of several applied fields but at the same time it has its own interesting theoretical problems. There exists a well-known axiomatic approach to fuzzy preference structures. In this axiomatic framework, general constructions of strict preference, indifference and incomparability relations associated with a fuzzy large preference relation are established via solving a system of functional equations. In the present paper we overview the solutions of two key functional equations (one of Frank, and one of Alsina) under different conditions on the underlying logic operations. Consequences of these general results to fuzzy preference modelling are also summarized

This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts’ knowledge are considered. Human preferences may be represented within the fuzzy preference relations and by applying diverse other preference formats. Considering this, so-called transformation functions reduce any preference format to fuzzy preference relations. This paper’s results can be applied independently or as part of a general approach to solving a wide class of problems with fuzzy coefficients, as well as within the framework of a general scheme of multi-criteria decision-making under conditions of uncertainty. The considered techniques for fuzzy preference modeling are directed at assessing, comparing, choosing, prioritizing, and/or ordering alternatives. These techniques have served to develop a computing system for multi-attribute decision-making. It has been implemented in the C# programming language, utilizing the “.NET” framework. The computing system allows one to represent decision-makers’ preferences in one of five preference formats. These formats and quantitative estimates are reduced to nonreciprocal fuzzy preference relations, providing homogeneous preference information for decision procedures. This paper’s results have a general character and were applied to analyze power engineering problems.

This paper proposes a gait phase classifier using a Recurrent Neural Network (RNN). Walking is a type of dynamic system, and as such it seems that the classifier made by using a general feed forward neural network structure is not appropriate. It is known that an RNN is suitable to model a dynamic system. Because the proposed RNN is simple, we use a back propagation algorithm to train the weights of the network. The input data of the RNN is the lower body's joint angles and angular velocities which are acquired by using the lower limb exoskeleton robot, ROBIN-H1. The classifier categorizes a gait cycle as two phases, swing and stance. In the experiment for performance verification, we compared the proposed method and general feed forward neural network based method and showed that the proposed method is superior.

A fuzzy multi-criteria decision-making model by associating technique for order preference by similarity to ideal solution with relative preference relation

The breaking of additively reciprocal property of fuzzy preference relations and its implication to decision making under uncertainty

In this paper we first consider the problem of extending fuzzy (weak and strict) preference relations, represented by fuzzy preorders on a set to a fuzzy preferences on subsets, and we characterise different possibilities. Based on their properties, we then semantically define and axiomatize several two-tiered graded modal logics to reason about the corresponding different notions of fuzzy preferences.

In this paper, the multi-person decision making problems with various different types of incomplete linguistic preference relations are studied. Some new concepts, including incomplete uncertain linguistic preference relation, incomplete triangular fuzzy linguistic preference relation, incomplete trapezoid fuzzy linguistic preference relation, expected incomplete linguistic preference relation and acceptable expected incomplete linguistic preference relation, are defined. Based on some transformation functions, all these types of incomplete linguistic preference relations are transformed into the expected incomplete linguistic preference relations. By using the additive consistency property, the acceptable expected incomplete linguistic preference relations are then extended to the complete linguistic preference relations. Moreover, an approach is proposed for multi-person decision making based on multiple types of incomplete linguistic preference relations including incomplete traditional linguistic preference relations, incomplete uncertain linguistic preference relations, incomplete triangular fuzzy linguistic preference relations and incomplete trapezoid fuzzy additive linguistic preference relations.

Additive consistent interval-valued Atanassov intuitionistic fuzzy preference relation and likelihood comparison algorithm based group decision making

On the basis of geometric consistency (GCI) of multiplicative preference relations and the transformation relation between multiplicative preference relations and fuzzy preference relation, inconsistency measure of fuzzy preference relation and the thresholds associated with it are studied. A new satisfactory consistency concept of fuzzy preference relation is defined. For a fuzzy preference relation, which does not possess satisfactory consistency, an improving consistency approach is provided. Then, programming models for estimating interval priority weights from fuzzy preference relations is established. Finally, an example is given to illustrate the validity and practicality of the proposed methods.

Normalizing rank aggregation method for priority of a fuzzy preference relation and its effectiveness

This chapter presents several approaches to derive a recommendation from a fuzzy relational preference model. Depending on the specificities of the decision problem, the recommendation can be a selection of the best alternatives, a ranking of these alternatives or a classification. In many decision problems involving several criteria and/or several experts with possibly conflicting evaluations of the alternatives, deriving a clear prescription is not an easy task since the overall fuzzy preference model is usually neither transitive nor complete. In this chapter, we present methods that have been specially designed to face this difficulty. We first consider the problem of choosing from a fuzzy preference relation. The classical notions of non-dominated alternatives and core are introduced (§ 3.1). Then several choice functions based on the use of scores, covering relations and kernels respectively are presented in sections (§ 3.2-3.5). Finally, a brief survey of ranking and sorting procedures is proposed (§ 3.6).KeywordsMembership FunctionPreference RelationChoice FunctionChoice RuleFuzzy RelationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This article proposes a quantified SWOT framework which integrates fuzzy linguistic preference relation to evaluate the competitive position of Abidjan container terminal in West Africa. The integration of fuzzy linguistic preference relation in the quantified SWOT framework circumvents the difficulties in terms of data collection and consistency of judgment matrices prevailing in the conventional AHP and Fuzzy AHP. Moreover, the study provides relevant evidences about the key internal and external attributes shipping lines considered as important and hence the attributes that ports/ terminals most need to enhance in order to improve their attractiveness and be well positioned in the market. On the other hand, the change in terminals competitive position between 2010 and 2013 highlights that the difficulties of West African ports in attracting customers in the sub-region are not only related to internal problems such as inadequate infrastructures, facilities or inefficiency, but significant political and economic constraint outside the ports constraints their competitiveness.

Some issues on consistency of fuzzy preference relations