A Novel Takagi–Sugeno Fuzzy System Modeling Method with Joint Feature Selection and Rule Reduction

2018 ◽  
Author(s):  
Defu Lin ◽  
Jun Wang ◽  
Jihua Zhu ◽  
Yifan Wang ◽  
Yizhang Jiang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fuzzy System ◽  
System Modeling ◽  
Modeling Method ◽  
Takagi Sugeno

Application of hydrologic forecast model

2012 ◽  
Vol 66 (2) ◽  
pp. 239-246
Author(s):  
Xu Hua ◽  
Xue Hengxin ◽  
Chen Zhiguo
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
System Modeling ◽  
Forecast Model ◽  
Training Data ◽  
New Method ◽  
Data Set ◽  
Gradient Learning ◽  
Takagi Sugeno

In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional TSK (Takagi-Sugeno-Kang) fuzzy inference training, this paper attempts to consider the TSK fuzzy system modeling approach based on the visual system principle and the Weber law. This approach not only utilizes the strong capability of identifying objects of human eyes, but also considers the distribution structure of the training data set in parameter regulation. In order to overcome the shortcoming of it adopting the gradient learning algorithm with slow convergence rate, a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The main advantage of this method lies in its very good optimization, very strong noise immunity and very good interpretability. The new method is applied to long-term hydrological forecasting examples. The simulation results show that the method is feasibile and effective, the new method not only inherits the advantages of traditional visual TSK fuzzy models but also has the better global convergence and accuracy than the traditional model.

scholarly journals Takagi-Sugeno Neuro-Fuzzy Modeling of a Multivariable Nonlinear Antenna System

2005 ◽  
Vol 2 (1) ◽  
pp. 12
Author(s):  
E. A. Al-Gallaf
Keyword(s):  
Dynamic System ◽  
Fuzzy System ◽  
System Modeling ◽  
Nonlinear Dynamic System ◽  
Fuzzy Modeling ◽  
Antenna System ◽  
Approximation Techniques ◽  
Intelligent Modeling ◽  
Neuro Fuzzy ◽  
Takagi Sugeno

This article investigates the use of a clustered based neuro-fuzzy system to nonlinear dynamic system modeling. It is focused on the modeling via Takagi-Sugeno (T-S) modeling procedure and the employment of fuzzy clustering to generate suitable initial membership functions. The T-S fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Compared to other well-known approximation techniques such as artificial neural networks, the employed neuro-fuzzy system has provided a more transparent representation of the nonlinear antenna system under study, mainly due to the possible linguistic interpretation in the form of rules. Created initial memberships are then employed to construct suitable T-S models. Furthermore, the T-S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). This intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of the fuzzy if-then rules. 

On interval-data based Type-1 Takagi-Sugeno fuzzy systems for uncertain nonlinear dynamic system identification

2016 ◽  
Vol 64 (6) ◽  
Author(s):  
Salman Zaidi ◽  
Andreas Kroll
Keyword(s):  
Fuzzy System ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic System ◽  
Random Variable ◽  
Interval Data ◽  
Fuzzy Modelling ◽  
Symbolic Data ◽  
Specific Assumption ◽  
Takagi Sugeno

AbstractA novel interval-data based Takagi-Sugeno fuzzy system is proposed to identify uncertain nonlinear dynamic systems by endowing the classical TS fuzzy system with probability theory and symbolic data analysis. Such systems have variability in their outputs, that is they produce varying responses each time when the same stimuli is applied to them under the same condition. Interval data is generated by repeating the identification experiment multiple times and applying the probabilistic techniques to get soft bounds of output. The interval data is then directly used in the TS fuzzy modelling, giving rise to interval antecedent and consequent parameters. This method does not require any specific assumption on the probability distribution of the random variable that models the uncertainty. The developed procedure is demonstrated for a pneumatic drive system.

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