Results 171 to 180 of about 24,496 (212)
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Takagi–Sugeno Fuzzy Control System
2009A nonlinear dynamic system can usually be represented by a set of nonlinear differential equations of the form $$ \dot{x}=f(x,u), (8.1) $$
Dan Huang, Sing Kiong Nguang
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2012
The Takagi-Sugeno systems (for short, to be denoted TS) are one of the most common fuzzy models. In such systems consequents are functions of inputs. This chapter shows a modification of such models as members of an classifier ensemble. The problem of incapability of merging several rule bases is addressed by a novel design of fuzzy systems ...
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The Takagi-Sugeno systems (for short, to be denoted TS) are one of the most common fuzzy models. In such systems consequents are functions of inputs. This chapter shows a modification of such models as members of an classifier ensemble. The problem of incapability of merging several rule bases is addressed by a novel design of fuzzy systems ...
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Genetic Takagi-Sugeno fuzzy reinforcement learning
Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206), 2002This paper presents two fuzzy reinforcement learning methods for solving complicated learning tasks of continuous domains. Takagi-Sugeno fuzzy reinforcement learning (TSFRL) is constructed by combining Takagi-Sugeno type fuzzy inference systems with Q-learning. Next, genetic Takagi-Sugeno fuzzy reinforcement learning (GTSFRL) is introduced by embedding
X.W. Yan, Z.D. Deng, Z.Q. Sun
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Sugeno fuzzy integral for finding fuzzy if–then classification rules
Applied Mathematics and Computation, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Sequential compactness for sets of sugeno fuzzy measures
Fuzzy Sets and Systems, 1987Pointwise sequential compactness for families of Sugeno measures is studied and a sufficient condition is proved for the equivalence between equiabsolute continuity, uniform order continuity and sequential compactness. A group-valued measure interpretation for \(\lambda\)-additive fuzzy measures is also shown.
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Competitive Takagi-Sugeno fuzzy reinforcement learning
Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204), 2002This paper proposes a competitive Takagi-Sugeno fuzzy reinforcement learning network (CTSFRLN) for solving complicated learning tasks of continuous domains. The proposed CTSFRLN is constructed by combining Takagi-Sugeno type fuzzy inference systems with action-value-based reinforcement learning methods.
X.W. Yan, Z.D. Deng, Z.Q. Sun
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Fuzzy Clip Detection Using Sugeno Model
NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society, 2006A machine vision based fuzzy inspection model for detecting present and missing clips is developed. Before arriving at this model different statistical approaches in image processing have been investigated and their capabilities to solve the problem are presented.
P. Mehran, K. Demirli, B. W. Surgenor
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On the Sugeno-type fuzzy observers
Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 2003In this paper, we present a fuzzy observer for nonlinear processes. It is obtained by "fuzzily interconnecting" local linear Luneburger observers. The approach uses techniques of robust and particularly quadratic stabilization to show the global quadratic stability of the fuzzy observer.
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Interval intuitionistic fuzzy-valued Sugeno integral
2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012The componentwise decomposition theorem of lattice-valued Sugeno integral is extended. The concepts of interval fuzzy-valued, intuitionistic fuzzy-valued and interval intuitionistic fuzzy-valued Sugeno integrals are introduced. It is shown that the intuitionistic fuzzy-valued Sugeno integrals and the interval fuzzy-valued Sugeno integrals are ...
Yongsheng Liu, Zhaojun Kong
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Bayesian Takagi–Sugeno–Kang Fuzzy Classifier
IEEE Transactions on Fuzzy Systems, 2017In this paper, the Takagi–Sugeno–Kang (TSK) fuzzy classifier is casted into the Bayesian inference framework and a new fuzzy classifier called Bayesian TSK fuzzy classifier (B-TSK-FC) is proposed accordingly. The proposed classifier can be constructed by learning both the antecedent and consequent parameters of the involved fuzzy rules simultaneously ...
Xiaoqing Gu, Fu-Lai Chung, Shitong Wang
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