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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) outpu
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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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For training Takagi-Sugeno fuzzy systems using the Levenberg-Marquardt method
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C++ codes for takagi-Sugeno fuzzy controller
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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
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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 Mam
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Takagi sugeno fuzzy modelling conroller with predefined sinusoidal function
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Takagi-Sugeno Fuzzy System by Recursive Least Square online method-Takagi-Sugeno Fuzzy System by Recursive Least Square online method
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training Takagi-Sugeno fuzzy systems using batch least squares
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training Takagi-Sugeno fuzzy systems using the Levenberg-Marquardt method
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The Sugeno-Takagi-like fuzzy controller:
This controller is a two input one output fuzzy controller
The first input is the error=x
The second input is the error_dot=y(time derivative of the error)
The output of the fuzzy controller is the CHA
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Takagi-Sugeno fuzzy models are based on the concept of fuzzy coding of information and operating with fuzzy sets instead of numbers
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• Batch least squares for training a Takagi-Sugeno fuzzy system
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Levenberg-Marquardt method for training a Takagi-Sugeno fuzzy system
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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
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模糊神经网络的实现 用于控制，优化等自动领域(Algoritmo de inferencia difusa ANFIS Takagi-sugeno)
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