The purpose of this paper is to present a genetic learning process for learning fuzzy control roles from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuz...

This paper proposes a fuzzy-rough method of maintaining CaseBased Reasoning (CBR) systems. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which take the form of fuzzy rules generated by the rough set technique. In paper [1], we have proposed a methodology for case base maintenance which ...

One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the propo...

expert systems can help to build banks customers' credit scoring models. here, selection of key features of the credit scoring is important. also, it is possible to express the features values as fuzzy. the problem is how to improve features selection by genetic algorithm, in way that these features can be employed as input in fuzzy expert system. this paper presents a hybrid credit scorin...

At present time configurable wireless sensor networks are given special consideration. Wireless sensor networks are now a static elements union. To make such a network a powerful system that is able to replace traditional computers, research is required of the application of different theories and technologies for sensor network development. However, building models of approximate reasoning and...

An extension of the Adaboost algorithm is proposed for obtaining fuzzy rule based classifiers from imprecisely perceived data. Isolated fuzzy rules are regarded as weak learners, and knowledge bases as ensembles. Rules are iteratively added to a base, and the search of the best rule at each iteration is carried out by a genetic algorithm driven by a fuzzy fitness function. The successive weight...

We propose a new admission control algorithm for GPRS/EGPRS cellular data networks, based on fuzzy inference. The admission decision is based on system parameters like the network load and users precedence, being expressed in the form of fuzzy if-then rules. Simulation results are presented for the fuzzy admission algorithm, using our model for a GPRS/EGPRS cell and a simplified simulation scen...

Fuzzy Logic Controllers Lee90] (FLCs) are being widely and successfully applied in diierent areas. Fuzzy Logic Controllers can be considered as knowledge-based systems, incorporating human knowledge into their Knowledge Base through Fuzzy Rules and Fuzzy Membership Functions (among other information elements). The deenition of these Fuzzy Rules and Fuzzy Membership Functions is actually aaected...

A general method is developed to generate fuzzy rules from numerical data. This new method consists of five steps: Step 1 divides the input and output spaces of the given numerical data into fuzzy regions; Step 2 generates fuzzy rules from the given data; Step 3 assigns a degree of each of the generated rules for the purpose of resolving conflicts among the generated rules; Step 4 creates a com...

Quality control charts indicate out of control conditions if any nonrandom pattern of the points is observed or any point is plotted beyond the control limits. Nonrandom patterns of Shewhart control charts are tested with sensitizing rules. When the processes are defined with fuzzy set theory, traditional sensitizing rules are insufficient for defining all out of control conditions. This is due...