designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing fuzzy learning classifier (flc) systems. conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. in thispaper new entities namely precision and recall from the field of information retrieval (ir)systems is adapted as alternative...

Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative...

In this paper, an iterative learning controller applying to linear discrete-time multivariable systems with variable initial conditions is investigated based on two-dimensional (2-D) system theory. The paper first introduces a 2-D tracking error system, and shows the effect of tracking errors against variable initial conditions. The sufficient conditions for the convergence of the learning cont...

predicting different behaviors in computer networks is the subject of many data mining researches. providing a balanced intrusion detection system (ids) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. many of the proposed methods perform well in one of the two aspects, and concentrate on a su...

In standard models of iterative thinking, there is a fixed rule hierarchy and every player chooses a fixed rule level. Nonequilibrium behavior emerges when some players do not perform enough thinking steps. Existing approaches however are inherently static. In this paper, we generalize models of iterative thinking to incorporate adaptive and sophisticated learning. Our model has three key featu...

This paper presents an intelligent iterative learning control scheme that is applicable to a class of nonlinear systems. The presented controller guarantees system stability by using a feedback controller coupled with an intelligent compensator and achieves precise tracking by using a set of iterative learning rules. In the feedback plus intelligent controller unit, the feedback control part st...

A method for building the rule-base of a fuzzy controller, using the iterative learning and adaptive neural fuzzy training is tested in practical conditions. This method aims to engage intelligent features to controller design procedure, by implying concepts and techniques from artificial intelligence as learning or adapting. An iterative self-learning algorithm is used to gather useful and tru...

SLAVE is an inductive learning algorithm that uses concepts based on fuzzy logic theory. This theory has been shown to be a useful representational tool for improving the understanding of the knowledge obtained from a human point of view. Furthermore, SLAVE uses an iterative approach for learning based on the use of a genetic algorithm (GA) as a search algorithm. In this paper, we propose a mod...