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fuzzy
- 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
FHSVD
- HankelToeplitz and Takagi Factorization Package
mohu
- 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)
lm_ts
- For training Takagi-Sugeno fuzzy systems using the Levenberg-Marquardt method
user
- C++ codes for takagi-Sugeno fuzzy controller
Takagi-Sugeno-FuzzyModelingforProcessControl
- 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
Takagi-Sugeno-fuzzymodel
- 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
takag_sugeno
- Takagi sugeno fuzzy modelling conroller with predefined sinusoidal function
kbcs
- it is a matlab code for takagi-sugeno modeling
rls_lip_ts
- Takagi-Sugeno Fuzzy System by Recursive Least Square online method-Takagi-Sugeno Fuzzy System by Recursive Least Square online method
bls_lip_ts
- training Takagi-Sugeno fuzzy systems using batch least squares
lm_ts
- training Takagi-Sugeno fuzzy systems using the Levenberg-Marquardt method
5-Fuzzy-TSK-Matlab
- Takagi-Sugeno fuzzy models are based on the concept of fuzzy coding of information and operating with fuzzy sets instead of numbers
TREENL
- takagi sugeno model for inverted pendulum
bls_lip_ts
- • Batch least squares for training a Takagi-Sugeno fuzzy system
lm_ts
- Levenberg-Marquardt method for training a Takagi-Sugeno fuzzy system
boilier identification using Takagi Sugeno
- 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
TS
- training Takagi-Sugeno fuzzy
PEM
- Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach(In this paper, the problem of robust fault diagnosis of proton exchange membrane (PEM) fuel cells is addressed by introducing the Takagi-S