Fuzzy Rules have been shown to be very useful in modeling relationships between variables that have a high degree of uncertainty or ambiguity. A major question in regards to learning fuzzy rule bases is how to handle interactions between rules of overlapping coverage. Structures, such as Yager’s HPS (Hierarchical Prioritized Structure), have been proposed to answer this question. In this paper,...

The present paper aims at a synthesis of belief revision theory with the Sneed formalism known as the structuralist theory of science. This synthesis is brought about by a dynamisation of classical structuralism, with an abductive inference rule and base generated revisions in the style of Rott (2001). The formalism of prioritised default logic (PDL) serves as the medium of the synthesis. Why s...

In this paper we present a simple belief updating system using recurrent fuzzy rules which improves class prediction in ordered datasets. The recurrent fuzzy rule builds up belief in a class for each point in a sample-ordered or timeordered dataset. Belief in each class is represented by a fuzzy set predicted class defined on the class universe. Belief in a class increases as positive cases are...

Dempster’s rule is traditionally interpreted as an operator for fusing belief functions. While there are different types of belief fusion, there has been considerable confusion regarding the exact type of operation that Dempster’s rule performs. Many alternative operators for belief fusion have been proposed, where some are based on the same fundamental principle as Dempster’s rule, and others ...

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only  considers both accuracy and generalization criteria in a single objective fu...

recently, tuning the weights of the rules in fuzzy rule-base classification systems is researched in order to improve the accuracy of classification. in this paper, a margin-based optimization model, inspired by support vector machine classifiers, is proposed to compute these fuzzy rule weights. this approach not only  considers both accuracy and generalization criteria in a single objective fu...

The problem of revising a belief database is treated in many classical works. We will consider here the problem of merging two belief databases (BDBs for short) Ψ1 and Ψ2, operation that will be denoted byΨ1 Ψ2, and whose result will be a new BDB. Since belief not necessarily reflects the actual state of the world (as opposed to knowledge), both BDBs could be incompatible. The goal is to constr...

A scientific environmental investment prediction plays a crucial role in controlling pollution and avoiding the blind of management. However effective usually has to fact three challenges about diversiform indicators, insufficient data, reliability models. In present study, new model is proposed using extended belief rule-based system (EBRBS) evidential reasoning (ER) rule, called ensemble EBRB...

We present a general framework for representing belief-revision rules and use it to characterize Bayes’s rule as a classical example and Je¤rey’s rule as a non-classical one. In Je¤rey’s rule, the input to a belief revision is not simply the information that some event has occurred, as in Bayes’s rule, but a new assignment of probabilities to some events. Despite their di¤erences, Bayes’s and J...

Dempster’s Rule is commonly described as an operator for fusing beliefs. While there are different possible interpretations of belief fusion, there is considerable confusion regarding the exact type of belief fusion that Dempster’s rule performs. Many alternative operators for belief fusion have been proposed, where some are based on the same fundamental principle as Dempster’s rule, and others...