This paper presents nonlinear model following control for a class of nonlinear systems using the fuzzy model-based control approach. We propose the construction method of augmented fuzzy control system for continuous-time nonlinear systems by differentiating the original nonlinear system. Moreover, we introduce the dynamic fuzzy controller which can make outputs of the nonlinear system converge...

Graph theory has an important role in the area of applications of networks and clustering‎. ‎In the case of dealing with uncertain data‎, ‎we must utilize ambiguous data such as fuzzy value‎, ‎fuzzy interval value or values of fuzzy number‎. ‎In this study‎, ‎values of fuzzy number were used‎. ‎Initially‎, ‎we utilized the fuzzy number value fuzzy relation and then proposed fuzzy number-value f...

A new approach is proposed for the data-based identification of transparent fuzzy rule-based classifiers. It is observed that fuzzy rule-based classifiers work in a similar manner as kernel function-based support vector machines (SVMs) since both model the input space by nonlinearly maps into a feature space where the decision can be easily made. Accordingly, trained SVM can be used for the con...

In this study, we introduce and study a concept of distributed fuzzymodeling. Fuzzy modeling encountered so far is predominantly of a centralizednature by being focused on the use of a single data set. In contrast to this style ofmodeling, the proposed paradigm of distributed and collaborative modeling isconcerned with distributed models which are constructed in a highly collaborativefashion. I...

migration of virtual machine (vm) is a critical challenge in cloud computing. the process to move vms or applications from one physical machine (pm) to another is known as vm migration. in vm migration several issues should be considered. one of the major issues in vm migration problem is selecting an appropriate pm as a destination for a migrating vm. to face this issue, several approaches are...

This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is int...