the fuzzy linear regression model with fuzzy input-output data andcrisp coefficients is studied in this paper. a linear programmingmodel based on goal programming is proposed to calculate theregression coefficients. in contrast with most of the previous works, theproposed model takes into account the centers of fuzzy data as animportant feature as well as their spreads in the procedure ofconstr...

We present construction of fuzzy S, utilizing the fact that CP is an S bundle over S. Fuzzy S is obtained by imposing an additional algebraic constraint on fuzzy CP. Hence, as in the case of fuzzy CP = SU(4)/U(3), fuzzy S is globally defined on R with the additional constraint on top of the construction of fuzzy CP. We consider the commutative limit of fuzzy S and find that it naturally gives c...

This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, U. S. A., were obtained. A portion of the data was used to train the ANFIS network, wh...

Based on a Takagi–Sugeno (T–S) fuzzy model and an inverse system method, this paper deals with the problem of actuator fault estimation for a class of nonlinear dynamic systems. Two different estimation strategies are developed. Firstly, T–S fuzzy models are used to describe nonlinear dynamic systems with an actuator fault. Then, a robust sliding mode observer is designed based on a T–S fuzzy m...

Fuzzy neural networks have been successfully applied to generate predictive rules for stocks forecasting. This paper presents a methodology for forecasting S&P 500 index based on the neural network with weighted fuzzy membership functions (NEWFM) and time series of S&P 500 index based on the defuzzyfication of weighted average method (The fuzzy model suggested by Takagi and Sugeno in 1985). NEW...

This paper mainly investigates fuzzy control with affine T-S delayed models, which can be applied to some nonlinear systems. Motivated from performing the linearization process associated with distinct operating points for nonlinear systems, an affine T-S fuzzy model with delayed state is addressed. The overall control then can be performed by the fuzzy inference mechanism, in which the consequ...

Fuzzy set theory, originally developed by Lotfi Zadeh in the 1960’s, has become a popular tool for control applications in recent years (Zadeh, 1965). Fuzzy control has been used extensively in applications such as servomotor and process control. One of its main benefits is that it can incorporate a human being’s expert knowledge about how to control a system, without that a person need to have...