Search - Takagi Sugeno

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[Other resource] AdaptiveFuzzyControlSystem DL : 1: ：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。
Update : 2008-10-13 Size : 242.4kb Publisher : daizhk
[Other resource] fuzzy DL : 0: 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。
Update : 2008-10-13 Size : 50.08kb Publisher : beyonddoor
[Software Engineering] AdaptiveFuzzyControlSystem DL : 0: ：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.
Update : 2025-02-19 Size : 242kb Publisher : daizhk
[AI-NN-PR] fuzzy DL : 0: 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.
Update : 2025-02-19 Size : 50kb Publisher : beyonddoor
[matlab] FHSVD DL : 0: HankelToeplitz and Takagi Factorization Package
Update : 2025-02-19 Size : 1kb Publisher : george
[AI-NN-PR] mohu DL : 0: 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)
Update : 2025-02-19 Size : 2kb Publisher : tiantian
[matlab] 75448176matlab_PID DL : 0: Update : 2025-02-19 Size : 203kb Publisher : 陈立
[matlab] lm_ts DL : 0: For training Takagi-Sugeno fuzzy systems using the Levenberg-Marquardt method
Update : 2025-02-19 Size : 2kb Publisher : ffault
[AI-NN-PR] user DL : 0: C++ codes for takagi-Sugeno fuzzy controller
Update : 2025-02-19 Size : 3kb Publisher : chiruri mae
[AI-NN-PR] Takagi-Sugeno-FuzzyModelingforProcessControl DL : 0: 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
Update : 2025-02-19 Size : 770kb Publisher : kiam
[AI-NN-PR] Takagi-Sugeno-fuzzymodel DL : 0: 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.
Update : 2025-02-19 Size : 187kb Publisher : kiam
[matlab] takag_sugeno DL : 0: Takagi sugeno fuzzy modelling conroller with predefined sinusoidal function
Update : 2025-02-19 Size : 318kb Publisher : sms1
[Software Engineering] Temperature-and-humidity-control-in-greenhouses-u DL : 0: Temperature and humidity control in greenhouses using the Takagi-Sugeno fuzzy model pdf document
Update : 2025-02-19 Size : 131kb Publisher : kub
[matlab] kbcs DL : 0: it is a matlab code for takagi-sugeno modeling
Update : 2025-02-19 Size : 187kb Publisher : mmkamani
[matlab] rls_lip_ts DL : 0: Takagi-Sugeno Fuzzy System by Recursive Least Square online method-Takagi-Sugeno Fuzzy System by Recursive Least Square online method
Update : 2025-02-19 Size : 2kb Publisher : Reza
[matlab] bls_lip_ts DL : 0: training Takagi-Sugeno fuzzy systems using batch least squares
Update : 2025-02-19 Size : 1kb Publisher : Reza
[matlab] lm_ts DL : 0: training Takagi-Sugeno fuzzy systems using the Levenberg-Marquardt method
Update : 2025-02-19 Size : 2kb Publisher : Reza
[Other] takagi-sugeno-modeling.pdf DL : 0: Fuzzy Identification of Systems and Its Applications to Modeling an Control
Update : 2025-02-19 Size : 9.75mb Publisher : 399
[Software Engineering] Takagi-Sugeno-Fuzzy-Logic-tutorial DL : 0: Takagi Sugeno Fuzzy Logic Tutorial that can help you learn.
Update : 2025-02-19 Size : 665kb Publisher : khalil
[matlab] boilier identification using Takagi Sugeno DL : 0: 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.
Update : 2025-02-19 Size : 149kb Publisher : orques

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