A decoupled fuzzy sliding-mode control for an aeroelastic system is derived. This aeroelastic dynamic system describes the nonlinear plunge and pitch motions of a wing section, using a single trailing-edge flap as the control input. The decoupled fuzzy sliding-mode control design method is proposed to simultaneously control both the plunge and pitch motions of the aeroelastic system. In this de...

Nowadays intelligent tools such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable methods for modeling an engineering system. This paper presents a new hybrid technique based on the combination of fuzzy inference system and artificial neural network for addressing navigational proble...

in this context and for helping to manufacturing industries in this research an attempt has been made to provide a method to managers of evaluating and ranking of agility strategies by using of a fuzzy inference system which is a branch of artificial intelligence. this research has been performed in three sequential phases. firstly, some variables, as factors of agility drivers, agility capabil...

In the current study two methods are evaluated for predicting the compressive strength of concrete containing metakaolin. Adaptive neuro-fuzzy inference system (ANFIS) model and stepwise regression (SR) model are developed as a reliable modeling method for simulating and predicting the compressive strength of concrete containing metakaolin at the different ages. The required data in training an...

nowadays, the customer's credit assessment is one of most important challenges for managers in decision making. especially in the current situation which the number scam has increased and outstanding claims of business company is increasing. on the other hand decision making about customers and their credit is based on expert judgment and also the customer information is often uncertain, a...

Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called as integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm converges and the tuning of fuzzy infer...

In this paper, a Sequential Adaptive Fuzzy Inference System called SAFIS is developed based on the functional equivalence between a radial basis function network and a fuzzy inference system (FIS). In SAFIS, the concept of “Influence” of a fuzzy rule is introduced and using this the fuzzy rules are added or removed based on the input data received so far. If the input data do not warrant adding...

prompt detection and diagnosis of faults in industrial systems areessential to minimize the production losses, increase the safety of the operatorand the equipment. several techniques are available in the literature to achievethese objectives. this paper presents fuzzy based control and fault detection for a6/4 switched reluctance motor. the fuzzy logic control performs like a classicalproporti...