data envelopment analysis operates as a tool for appraising the relative efficiency of a set of homogenous decision making units. this methodology is applied widely in different contexts. regarding to its logic, dea allows each dmu to take its optimal weight in comparison with other dmus while a similar condition is considered for other units. this feature is a bilabial characteristic which opt...

Adaptive Neuro-Fuzzy Inference System is growing to predict nonlinear behaviour of construction materials. However due to wide variety of parameters in this type of artificial intelligent machine, selecting the proper optimization methods together with the best fitting membership functions strongly affect the accuracy of prediction. In this study the non-linear relation between splitting tensil...

This paper studies the problem of dynamic output feedback H∞ control of discrete-time Takagi–Sugeno (T–S) fuzzy systems via a switched dynamic parallel distributed compensation (DPDC) scheme. The considered switched DPDC control structure is determined based on the values of membership functions. Some new properties of membership functions are obtained, and exploited to derive sufficient condit...

Current fuzzy control research tries to obtain the less conservative conditions to prove stability and performance of fuzzy control systems. In many fuzzy models, membership functions with multiple arguments are defined as the product of simpler ones, where all possible combinations of such products conform a fuzzy partition. In particular, such situation arises with widely-used fuzzy modelling...

In this paper, we discuss fuzzy classifiers based on Kernel Discriminant Analysis (KDA) for two-class problems. In our method, first we employ KDA to the given training data and calculate the component that maximally separates two classes in the feature space. Then, in the one-dimensional space obtained by KDA, we generate fuzzy rules with one-dimensional membership functions and tune the slope...

The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh ...

AbsrractConventional fuzzy logic controller uses input membership functions that consist of identical triangular input membership functions (MF) for all the subsets that are not located at the two ends of the universal set (center width constant or CWC). Some researchers in the field of motor controllers have started using narrower base triangular membership functions for the input subsets that...

This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting the surface roughness in turning operation for set of given cutting parameters, namely cutting speed, feed rate and depth of cut. Two different membership functions, triangular and bell shaped, were adopted during the training process of ANFIS in order to compare the prediction accuracy of surface roughness by t...

In least squares support vector machines (LS-SVMs), the optimal separating hyperplane is obtained by solving a set of linear equations instead of solving a quadratic programming problem. But since SVMs and LS-SVMs are formulated for two-class problems, unclassifiable regions exist when they are extended to multiclass problems. In this paper, we discuss fuzzy LS-SVMs that resolve unclassifiable ...

In this dissertation the generation and tuning of fuzzy membership function parameters are considered as a part of the fuzzy model development process. The automatic generation and tuning of fuzzy membership function parameters are needed for the fast adaptation and tuning of fuzzy models of various nonlinear dynamical systems. The developed methods are especially useful in automatic fuzzy memb...