نتایج جستجو برای: fuzzy type 2

تعداد نتایج: 3537834  

Journal: :Inf. Sci. 2012
Sarah Greenfield Francisco Chiclana Robert Ivor John Simon Coupland

For generalised type-2 fuzzy sets the defuzzification process has historically been slow and inefficient. This has hampered the development of type-2 Fuzzy Inferencing Systems for real applications and therefore no advantage has been taken of the ability of type-2 fuzzy sets to model higher levels of uncertainty. The research reported here provides a novel approach for improving the speed of de...

Journal: :Evolving Systems 2012
Sevil Ahmed Nikola Georgiev Shakev Andon V. Topalov Kostadin Borisov Shiev Okyay Kaynak

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties and to perform under noisy conditions in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for interval type-2 Takagi–Sugeno–Kang (TSK) fuzzy neural networks is proposed in this paper. Differently from the other rece...

2014
Chun - Ying Lee Ying Liu Chiang - Ho Cheng

The main objective of this article is to present the semi-active vibration control using an electro-rheological fluid embedded sandwich structure for a cantilever beam. ER fluid is a smart material, which cause the suspended particles polarize and connect each other to form chain. The stiffness and damping coefficients of the ER fluid can be changed in 10 micro seconds; therefore, ERF is suitab...

Journal: :J. Applied Mathematics 2012
Ll Yi-Min Yue Yang Li Li

A novel indirect adaptive backstepping control approach based on type-2 fuzzy system is developed for a class of nonlinear systems. This approach adopts type-2 fuzzy system instead of type-1 fuzzy system to approximate the unknown functions. With type-reduction, the type-2 fuzzy system is replaced by the average of two type-1 fuzzy systems. Ultimately, the adaptive laws, by means of backsteppin...

2009
Hooman Tahayori Giovanni Degli Antoni

about to represent. Once the membership function has been established (estimated or defined), the concept is described very precisely as the membership values are exact numerical quantities. This seems to raise a certain dilemma of excessive precision in describing imprecise phenomena. In fact, this concern has already sparked a lot of debates starting from the very inception of fuzzy sets.” Co...

2015
M. H. Fazel Zarandi R. Gamasaee

Supply chain management; Fuzzy clustering; Interval type-2 fuzzy hybrid system; Demand forecasting; Ordering policy; Bullwhip effect. Abstract The purpose of this paper is to evaluate and reduce the bullwhip effect in fuzzy environments by means of type-2 fuzzymethodology. In order to reduce the bullwhip effect in a supply chain, we propose a newmethod for demand forecasting. First, the demand ...

Journal: :Remote Sensing 2015
Maryam Nikfar Mohammad Javad Valadan Zoej Mehdi Mokhtarzade Mahdi Aliyari Shoorehdeli

The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle ...

2014
Suman Lata Mohammad Ayyub

This paper presents the theory and design of interval type-2 fuzzy logic systems (FLSs) because of computational complexity of using general type-2 fuzzy set (T2FS) in type-2 fuzzy system. We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs; one that is based on a general inference formula for them. We introduce the concept of up...

2017
Ji-Hwan Hwang Young-Chang Kang Jong-Wook Park Dong W. Kim

In this paper, advanced interval type-2 fuzzy sliding mode control (AIT2FSMC) for robot manipulator is proposed. The proposed AIT2FSMC is a combination of interval type-2 fuzzy system and sliding mode control. For resembling a feedback linearization (FL) control law, interval type-2 fuzzy system is designed. For compensating the approximation error between the FL control law and interval type-2...

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