Moved by the need increased for modeling of the fuzzy data, the success of the systems of exact generation of summary of data, we propose in this paper, a new approach of generation of summary from fuzzy data called “FuzzySaintEtiQ”. This approach is an extension of the SaintEtiQ model to support the fuzzy data. It presents the following optimizations such as 1) the minimization of the expert r...

Information Systems, and in particular Current Research Information Systems (CRISs), are usually quite difficult to query when looking for specific information, due to the huge amounts of data they contain. To solve this problem, we propose to use a personal search agent that uses fuzzy and rough sets to inform the user about newly available information. Additionally, in order to automate the o...

This paper proposes a novel adaptive control law for nonlinear systems using Takagi-Sugeno fuzzy system. Takagi-Sugeno fuzzy system is used to identify nonlinear system components theta alpha and theta beta. Stable Indirect Adaptive control law is such that it has two control components one is certainty equivalence control and other is sliding mode control. Sliding mode controller is used to en...

Load balancing in distributed computer systems is the process of redistributing the work load among processors in the system to improve system performance. Most of previous research in using fuzzy logic for the purpose of load balancing has only concentrated in utilizing fuzzy logic concepts in describing processors load and tasks execution length. The responsibility of the fuzzy-based load bal...

− A new trend in the design of fuzzy rulebased systems is the use of evolutionary multiobjective optimization (EMO) algorithms. This trend is observed in various areas in machine learning. EMO algorithms are often used to search for a number of Pareto-optimal non-linear systems with respect to their accuracy and complexity. In this paper, we first explain some basic concepts in multiobjective o...

Neural Networks (NN), Type-1 Fuzzy Logic Systems (T1FLS) and Interval Type-2 Fuzzy Logic Systems (IT2FLS) are universal approximators, they can approximate any non-linear function. Recent research shows that embedding T1FLS on an NN or embedding IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect information. In this paper we...

Fuzzy logic control systems are structured numerical estimators. They combine both the numerical process and human like reasoning. Neural networks are numerical trainable dynamical systems that are able to emulate human brain functions; their connectionist structure can be used to find the proper parameters and structures that resemble human thinking rules for fuzzy logic controllers. Generally...

The advantage of solving the complex nonlinear problems by utilizing fuzzy logic methodologies is that the experience or expert’s knowledge described as a fuzzy rule base can be directly embedded into the systems for dealing with the problems. The current limitation of appropriate and automated designing of fuzzy controllers are focused in this paper. The structure discovery and parameter adjus...

A preconditioned Jacobi (PJ) method is provided for solving fuzzy linear systems whose coefficient matrices are crisp M matrices and the right-hand side columns are arbitrary fuzzy number vectors. The iterative algorithm is given for the preconditioned Jacobi method. The convergence is analyzed with convergence theorems. Numerical examples are given to illustrate the procedure and show the effe...

The domain model is one of the important components used by adaptive learning systems to automatically generate customized courses for the learners. In this paper our contribution is to propose a new tool for implementation of a domain model based on fuzzy relationships among concepts. This tool allows the experts and teachers to find the best parameters in order to adapt the learners’ differen...