نتایج جستجو برای: mamdani fuzzy inference model
تعداد نتایج: 2230636 فیلتر نتایج به سال:
TYPE-2 fuzzy sets (T2 FSs), originally introduced by Zadeh [3], provide additional design degrees of freedom in Mamdani and TSK fuzzy logic systems (FLSs), which can be very useful when such systems are used in situations where lots of uncertainties are present [4]. The implementation of this type-2 FLS involves the operations of fuzzification, inference,and output processing. We focus on ―outp...
Analytical structure for a fuzzy PID controller is introduced by employing two fuzzy sets for each of the three input variables and four fuzzy sets for the output variable. This structure is derived via left and right trapezoidal membership functions for inputs, trapezoidal membership functions for output, algebraic product triangular norm, bounded sum triangular co-norm, Mamdani minimum infere...
This paper proposes a novel approach for the classification of phonocardiograms based on statistical properties of the PCG signal energy envelograms using fuzzy inference system. Fuzzification of features is done to remove absolute boundaries and assign a degree of association to every segment of the signal with the corresponding heart sound. Since heart sound signals are highly nonstationary, ...
Edge Detection is an important task for sharpening the boundary of images to detect the region of interest. This paper applies a linear cellular automata rules and a Mamdani Fuzzy inference model for edge detection in both monochromatic and the RGB images. In the uniform cellular automata a transition matrix has been developed for edge detection. The Results have been compared to the ...
This paper describes FPGA realization of a Fuzzy Temperature Controller (FTC) using VHDL intended for industrial application. The system is built up with four major modules namely fuzzification, inference, implication and defuzzification. The composition method selected for the fuzzy model is the MaxMin composition while the Mamdani Min operator is chosen as the implication method. Each module ...
One of the fundamental problems in wireless sensor networks (WSNs) is localization that forms the basis for many location aware applications. Localization in WSNs is to determine the physical position of sensor node based on the known positions of several nodes. In this paper, a range free, enhanced weighted centroid localization method using edge weights of adjacent nodes is proposed. In the p...
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