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IEEE Transactions on Fuzzy Systems
IF
8.76
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2904
Papers 2883
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Published on Jan 1, 2018in IEEE Transactions on Fuzzy Systems 8.76
Changzhu Zhang3
Estimated H-index: 3
,
Hak-Keung Lam43
Estimated H-index: 43
+ 2 AuthorsQijun Chen7
Estimated H-index: 7
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Published on Jan 1, 2018in IEEE Transactions on Fuzzy Systems 8.76
Rong Gao7
Estimated H-index: 7
,
Dan A. Ralescu26
Estimated H-index: 26
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Published on Jan 1, 2018in IEEE Transactions on Fuzzy Systems 8.76
Jun-Wei Wang13
Estimated H-index: 13
(USTB: University of Science and Technology Beijing),
Han-Xiong Li44
Estimated H-index: 44
(CityU: City University of Hong Kong)
This paper presents a Lyapunov and partial differential equation (PDE)-based methodology to solve static collocated piecewise fuzzy control design of quasi-linear parabolic PDE systems subject to periodic boundary conditions. Two types of piecewise control, i.e., globally piecewise control and locally piecewise control are considered, respectively. A Takagi–Sugeno (T–S) fuzzy PDE model that is constructed via local sector nonlinearity method is first employed to accurately describe spatiotempora...
1 Citations Source Cite
Published on Jan 1, 2018in IEEE Transactions on Fuzzy Systems 8.76
Carmen Torres-Blanc7
Estimated H-index: 7
,
Susana Cubillo Villanueva9
Estimated H-index: 9
,
Pablo Hernández-Varela
Type-2 fuzzy sets (T2FSs) were introduced by L. A. Zadeh in 1975 as an extension of type-1 fuzzy sets (T1FSs). In this extension, the degree to which an element belongs to a set is just a label of the linguistic variable “TRUTH,” which allows to represent reality in a more appropriate way. On the other hand, negations play an essential role within fuzzy sets theory. In fact, they are necessary in order to obtain, for example, complements of fuzzy sets, dual of a t-norm or a t-conorm, entropies, ...
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Published on Jan 1, 2018in IEEE Transactions on Fuzzy Systems 8.76
Linlin Chen (NCEPU: North China Electric Power University), Degang Chen24
Estimated H-index: 24
(NCEPU: North China Electric Power University),
Hui Wang29
Estimated H-index: 29
(Ulster University)
Fuzzy similarity relation is a function to measure the similarity between two samples. It is widely used to learn knowledge under the framework of fuzzy machine learning. The selection of a suitable fuzzy similarity relation is important for the learning task. It has been pointed out that fuzzy similarity relations can be brought into the framework of kernel functions in machine learning. This fact motivates us to study fuzzy similarity relation selection for fuzzy machine learning utilizing ker...
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Published on Jan 1, 2018in IEEE Transactions on Fuzzy Systems 8.76
Hong-Gui Han16
Estimated H-index: 16
(Beijing University of Technology),
Xiaolong Wu2
Estimated H-index: 2
(Beijing University of Technology)
+ 1 AuthorsJunfei Qiao19
Estimated H-index: 19
(Beijing University of Technology)
Fuzzy neural networks (FNNs), with suitable structures, have been demonstrated to be an effective tool in approximating nonlinearity between input and output variables. However, it is time consuming to construct a FNN with appropriate number of fuzzy rules to ensure its generalization ability. To solve this problem, an efficient optimization technique is introduced in this paper. First, a self-adaptive structural optimal algorithm (SASOA) is developed to minimize the structural risk of FNN, lead...
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Published on Jan 1, 2018in IEEE Transactions on Fuzzy Systems 8.76
Raúl Pérez-Fernández6
Estimated H-index: 6
,
Bernard De Baets55
Estimated H-index: 55
Penalty functions have been a common tool in data aggregation for decades. Unfortunately, although the definition of a penalty function has evolved over the years, the use of penalty functions has been reduced to the aggregation of real numbers. However, in this ‘era of aggregation,’ the need of generalizing the current definition in order to comply with the characteristics of new types of data arises. In this paper, we bring to the attention the notion of betweenness relation and propose to rep...
4 Citations Source Cite
Published on Jan 1, 2018in IEEE Transactions on Fuzzy Systems 8.76
Zhenyu Lu4
Estimated H-index: 4
(NPU: Northwestern Polytechnical University),
Panfeng Huang16
Estimated H-index: 16
(NPU: Northwestern Polytechnical University)
+ 1 AuthorsHaifei Chen (NPU: Northwestern Polytechnical University)
In this paper, a novel fuzzy-observer-based hybrid force/position control method is investigated for a bimanual teleoperation system in the presence of dynamics uncertainties, random network-induced time delays, and multiple sampling rates of remote control signals and local measured data. The system structure consists of two pairs of position observers and contact force/torque estimators. The position observers are designed based on Takagi–Sugeno fuzzy inference rules to estimate the delayed re...
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Published on Jan 1, 2018in IEEE Transactions on Fuzzy Systems 8.76
Yan Zhao , Huijun Gao85
Estimated H-index: 85
+ 0 AuthorsJianbin Qiu
In this work, the fuzzy observer-based control problem is investigated for a class of nonlinear coupled systems, which consists of a hyperbolic partial differential equation (PDE) containing nonlinearities and a nonlinear ordinary differential equation (ODE). The nonlinear coupled system is represented as a Takagi–Sugeno (T–S) fuzzy coupled hyperbolic PDE-ODE model. Based on the T–S fuzzy model, a novel Lyapunov functional approach is proposed to design a fuzzy observer based control strategy. M...
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