نتایج جستجو برای: Evolutionary Fuzzy System
تعداد نتایج: 2388789 فیلتر نتایج به سال:
Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...
Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...
This study presents a novel intelligent Fuzzy Genetic Differential Evolutionary model for the optimization of a fuzzy expert system applied to heart disease prediction in order to reduce the risk of heart disease. To this end, a fuzzy expert system has been proposed for the prediction of heart disease. The proposed model can be used as a tool to assist physicians. In order to: (1) tune the para...
this paper introduces a novel control methodology based on fuzzy controller for a glucose-insulin regulatory system of type i diabetes patient. first, in order to incorporate knowledge about patient treatment, a fuzzy logic controller is employed for regulating the gains of the basis proportional-integral (pi) as a self-tuning controller. then, to overcome the key drawback of fuzzy logic contro...
This paper introduces a novel control methodology based on fuzzy controller for a glucose-insulin regulatory system of type I diabetes patient. First, in order to incorporate knowledge about patient treatment, a fuzzy logic controller is employed for regulating the gains of the basis Proportional-Integral (PI) as a self-tuning controller. Then, to overcome the key drawback of fuzzy logic contro...
The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a “data mining-based evolutionary fuzzy expert system” (DEFE...
Soft computing models based on intelligent fuzzy systems have the capability of managing uncertainty in the image based practices of disease. Analysis of the breast tumors and their classification is critical for early diagnosis of breast cancer as a common cancer with a high mortality rate between women all around the world. Soft computing models based on fuzzy and evolutionary algorithms play...
An evolutionary fuzzy controller that drives the output parameters of fuzzy logic system in such a way that the Lyapunov function and the system is exponentially stable is presented. The output parameters of the fuzzy logic system are updated for every sampling time in an evolutionary manner. Only part of the parameters associated with the current situation are considered and, thus, the present...
fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. in this research, fcm is chosen for fuzzy clustering. parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. these two parameters require tuning to reduce the overfitting in the...
Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called as integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm converges and the tuning of fuzzy infer...
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