Non-symmetric Membership Function Research Articles

Detecting Moving Objects Based on Fuzzy Non- Symmetric Membership Function in Color Space

In this paper, we address the problem about detecting moving objects in a video stream obtained by a fixed camera. To detect the moving objects, a method is proposed using the fuzzy Non-symmetric membership function  in color space. To evaluate the performances of moving detection, the performance comparisons of different space and different functions are carried out. The experiment results show good performance of our method for detecting moving objects.

Stability Margin of Linear Systems With Parameters Described by Fuzzy Numbers

This paper deals with the linear systems with uncertain parameters described by fuzzy numbers. The problem of determining the stability margin of those systems with linear affine dependence of the coefficients of a characteristic polynomial on system parameters is studied. Fuzzy numbers describing the system parameters are allowed to be characterized by arbitrary nonsymmetric membership functions. An elegant solution, graphical in nature, based on generalization of the Tsypkin-Polyak plot is presented. The advantage of the presented approach over the classical robust concept is demonstrated on a control of the Fiat Dedra engine model and a control of the quarter car suspension model.

Fuzzy probability measures (FPM) based non-symmetric membership function: Engineering examples of ground subsidence due to underground mining

This paper introduces a new FPM method for prediction of surface subsidence due to inclined coal seam mining. Based on the non-symmetric membership function and the definition of the fuzzy probability measure, the mathematical model for the two-dimensional problem is developed and applied to the analysis of ground subsidence due to underground mining of inclined coal seam. The results of calculation with the new model indicate that the predicted value has good agreement with the measured data.

DETECTING MOVING OBJECTS USING FUZZY INTEGRATION MOMENT

Moments are widely used in pattern recognition, image processing, computer vision and multi-resolution analysis. It can also be use to detect the moving objects. In this paper, we address the problems about detecting moving objects in a video stream obtained by a fixed camera. To detect the moving objects, a method is proposed using the fuzzy integrating moment that combines the 1st, 3rd and 5th order temporal orthogonal Gaussian-Hermite moments and it takes into account the non-symmetric membership function π. To evaluate the performances of moving detection, the performance comparisons of different methods are carried out. The experiment results show good performance of our method for detecting moving objects.

A PID type fuzzy logic controller: Design, performance evaluation and applications with PLCs on long dead-time systems

It has been widely verified that a fuzzy logic controller can achieve a better performance for industrial systems than conventional controllers. In addition, fuzzy logic controllers have a major interest of researchers in the area of industrial process control. For these reasons in this paper, a PID type fuzzy logic controller (FPID) is presented for programmable logic controllers (PLC), and the design steps are explained in detail (such as tuning FPID's parameters, using symmetric or non-symmetric membership functions) to express the presented FPID functional for industrial systems. The aim of this study is to put forward efficiency of PID type fuzzy logic controllers for PLCs on industrial long dead-time systems. Moreover in this paper, a PLC algorithm is constructed and the complete algorithm is tested in a first order long dead-time system, second order long dead-time systems, and results of this tests are offered and explained.

Learning feed-forward and recurrent fuzzy systems: A genetic approach

In this paper, we present a new learning method for rule-based feed-forward and recurrent fuzzy systems. Recurrent fuzzy systems have hidden fuzzy variables and can approximate the temporal relation embedded in dynamic processes of unknown order. The learning method is universal i.e., it selects optimal width and position of Gaussian like membership functions and it selects a minimal set of fuzzy rules as well as the structure of the rules. A genetic algorithm (GA) is used to estimate the fuzzy systems which capture low complexity and minimal rule base. Optimization of the “entropy” of a fuzzy rule base leads to a minimal number of rules, of membership functions and of subpremises together with an optimal input/output (I/O) behavior. Most of the resulting fuzzy systems are comparable to systems designed by an expert but offers a better performance. The approach is compared to others by a standard benchmark (a system identification process). Different results for feed-forward and first-order recurrent fuzzy systems with symmetric and non-symmetric membership functions are presented.