To control structures against wind and earthquake excitations, Adaptive Neuro Fuzzy Inference Systems and Neural Networks are combined in this study. The control scheme consists of an ANFIS inverse model of the structure to assess the control force. Considering existing ANFIS controllers, which require a second controller to generate training data, the authors’ approach does not need anot...

Abstract: In this paper, we present a fuzzy model based indirect adaptive control scheme for a class of nonlinear systems with a dead-zone. The Takagi-Sugeno (TS) fuzzy model is used for representing a nonlinear system, where the parameters of the fuzzy model are updated online according to Lyapunov stability theorem. An inverse functions are cascaded with the plant to cancel the effects of dea...

In this note the notion of interval-valued fuzzy B-algebras (briefly,i-v fuzzy B-algebras), the level and strong level B-subalgebra is introduced.Then we state and prove some theorems which determine the relationshipbetween these notions and B-subalgebras. The images and inverse images ofi-v fuzzy B-subalgebras are defined, and how the homomorphic images andinverse images of i-v fuzzy B-subalge...

This paper deals with adaptive control of nonlinear dynamic systems preceded by unknown backlash-like hysteresis nonlinearities, where the hysteresis is described by a dynamic equation. By utilizing this dynamic model and by combining a fuzzy universal function approximator with adaptive control techniques, a stable adaptive fuzzy control algorithm is developed without constructing a hysteresis...

In this paper, we introduce an approach by using inverse fuzzy transforms based on the fuzzy partition with combination in collocation technique for the numerical solution of Fredholm integral equations of the second kind. The main advantage of this approach is to reduce the problem to the linear system of equations. We present the convergence theorem for this method. Finally, we give two examp...

Abstract: In this paper, the estimation of Fuzzy Linear Regression (FLR) is computed by using leastsquare approach. For using this approach, a solution of Fuzzy Linear System (FLS) is required. This solution is computed by applying an iterative method (Huang’s Algorithm) which this alternative method is powerful to compute the solutions without using the inverse of coefficient matrix. Experimen...

This paper develops a decentralized adaptive fuzzy control scheme for robot manipulators via a combination of genetic algorithm and gradient method. The controller for each link consists of a feedforward fuzzy torque-computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line by an improved genetic algorithm, that is to say, not only the param...

This paper presents a new way to design a Fuzzy Terminal Iterative Learning Control (TILC) to control the heater temperature setpoints of a thermoforming machine. This fuzzy TILC is based on the inverse of a fuzzy model of this machine, and is built from experimental (or simulation) data with kriging interpolation. The Fuzzy Inference System usually used for a fuzzy model is the zero order Taka...

In this note the notion of interval-valued fuzzy BF -algebras (briefly, i-v fuzzy BF -algebras), the level and strong level BF -subalgebra is introduced. Then we state and prove some theorems which determine the relationship between these notions and BF -subalgebras. The images and inverse images of i-v fuzzy BF -subalgebras are defined, and how the homomorphic images and inverse images of i-v ...

In this note the notion of interval-valued fuzzy QS-algebra (briefly, i-v fuzzy QS-algebra), as well as the i-v level and strong i-v level QS-subalgebra is introduced. Several theorems which determine the relationship between these notions and QS-subalgebras are stated and proved. The images and inverse images of i-v fuzzy QS-subalgebras are defined, and how the homomorphic images and inverse i...