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Description: ：运用动力学原理建立了小车-倒摆的仿真模型, 并以对象输入输出的测试数据为依据，讨
论了Takagi-Sugeno 模糊模型的参数辨识，提出了模糊逆模型控制方案，基于此借助Matlab 的
Simulink 设计了小车-倒摆的动态模型及其模糊自适应控制系统。仿真结果证明了本文采用的控制
策略的有效性。
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Size: 248215 |
Author: daizhk |
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Description: The neuro-fuzzy software for identification and data analysis has been implemented in the MATLAB language ver. 4.2.
The software trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a training set of N (single) output-(multi) input samples.
The returned model has the form
1) if input1 is A11 and input 2 is A12 then output =f1(input1,input2)
2) if input1 is A21 and input 2 is A22 then output =f2(input1,input2)
看不懂，据高手说，非常有用。
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Size: 51282 |
Author: beyonddoor |
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Description: ：运用动力学原理建立了小车-倒摆的仿真模型, 并以对象输入输出的测试数据为依据，讨
论了Takagi-Sugeno 模糊模型的参数辨识，提出了模糊逆模型控制方案，基于此借助Matlab 的
Simulink 设计了小车-倒摆的动态模型及其模糊自适应控制系统。仿真结果证明了本文采用的控制
策略的有效性。-: The use of dynamic theory to establish a car- inverted pendulum simulation model and the object input and output test data as the basis to discuss the Takagi-Sugeno fuzzy model parameter identification, the fuzzy inverse model control scheme, based on the use of Matlab Simulink design of the car- inverted pendulum dynamic model and the fuzzy adaptive control system. Simulation results show this paper, the effectiveness of the control strategy.
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Size: 247808 |
Author: daizhk |
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Description: The neuro-fuzzy software for identification and data analysis has been implemented in the MATLAB language ver. 4.2.
The software trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a training set of N (single) output-(multi) input samples.
The returned model has the form
1) if input1 is A11 and input 2 is A12 then output =f1(input1,input2)
2) if input1 is A21 and input 2 is A22 then output =f2(input1,input2)
看不懂，据高手说，非常有用。-The neuro-fuzzy software for identification and data analysis has been implemented in the MATLAB language ver. 4.2. The software trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a training set of N (single) output-( multi) input samples.The returned model has the form1) if input1 is A11 and input 2 is A12 then output = f1 (input1, input2) 2) if input1 is A21 and input 2 is A22 then output = f2 (input1, input2) can not read, according to experts said that very useful.
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Size: 51200 |
Author: beyonddoor |
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Description: HankelToeplitz and Takagi Factorization Package
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Size: 1024 |
Author: george |
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Description: 高木关野模糊系统（将高木关野模糊系统应用到BP神经网络中）-Takagi Sugeno fuzzy system (to Takagi Sugeno fuzzy system applied to the BP neural network)
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Size: 2048 |
Author: tiantian |
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Description:
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Size: 207872 |
Author: 陈立 |
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Description: For training Takagi-Sugeno fuzzy systems using the Levenberg-Marquardt method
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Size: 2048 |
Author: ffault |
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Description: C++ codes for takagi-Sugeno fuzzy controller
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Size: 3072 |
Author: chiruri mae |
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Description: 2:Takagi-Sugeno fuzzy modeling
2.1 Construction of Fuzzy Models
2.1.1 Sector Nonlinearity
2.2 Basic Fuzzy Mathematics for Modeling
2.2.1 Local Approximation in Fuzzy
Partition Spaces-2:Takagi-Sugeno fuzzy modeling
2.1 Construction of Fuzzy Models
2.1.1 Sector Nonlinearity
2.2 Basic Fuzzy Mathematics for Modeling
2.2.1 Local Approximation in Fuzzy
Partition Spaces
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Size: 788480 |
Author: kiam |
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Description: The fuzzy inference process discussed so far is Mamdani s fuzzy inference method, the most common methodology. This section discusses the so-called Sugeno, or Takagi-Sugeno-Kang, method of fuzzy inference. Introduced in 1985, it is similar to the Mamdani method in many respects. The first two parts of the fuzzy inference process, fuzzifying the inputs and applying the fuzzy operator, are exactly the same. The main difference between Mamdani and Sugeno is that the Sugeno output membership functions are either linear or constant.
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Size: 191488 |
Author: kiam |
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Description: Takagi sugeno fuzzy modelling conroller with predefined sinusoidal function
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Size: 325632 |
Author: sms1 |
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Description: Temperature and humidity control in greenhouses using the Takagi-Sugeno fuzzy model pdf document
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Size: 134144 |
Author: kub |
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Description: it is a matlab code for takagi-sugeno modeling
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Size: 191488 |
Author: mmkamani |
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Description: Takagi-Sugeno Fuzzy System by Recursive Least Square online method-Takagi-Sugeno Fuzzy System by Recursive Least Square online method
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Size: 2048 |
Author: Reza |
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Description: training Takagi-Sugeno fuzzy systems using batch least squares
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Size: 1024 |
Author: Reza |
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Description: training Takagi-Sugeno fuzzy systems using the Levenberg-Marquardt method
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Size: 2048 |
Author: Reza |
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Description: Fuzzy Identification of Systems and Its Applications to Modeling an Control
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Size: 10226688 |
Author: 399 |
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Description: Takagi Sugeno Fuzzy Logic Tutorial that can help you learn.
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Size: 680960 |
Author: khalil |
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Description: This paper describes the application of an identification algorithm clustering type Gustafson-Kessel nonlinear
dynamical system. From input-output data the algorithm generates fuzzy models of Takagi-Sugeno. This type of modeling is applied
to a non-linear numerical model. The non-linear input / output model of the system is decomposed in several described by
membership functions and fuzzy rule-based local linear systems. The results are presented and prospects for future work.
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Size: 152576 |
Author: orques
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