In this paper, we propose, simulated and modelled, first time, a novel fuzzy inference processor capable of handling three types of membership functions together. To implement the proposed inference processor, a novel multi membership function (MMF) MAX-MIN calculator circuit, calculating the matching degree (MD) between three types of membership functions (MF): Gaussian, Trapezoid and Triangul...

An adaptive fuzzy sliding mode controller (AFSMC) is adopted to reduce the 2D flow-induced vibration of an elastically supported square-section cylinder, free to oscillate in stream-wise andtransverse directions in both lock-in and galloping regions. The AFSMC strategy consists of a fuzzy logic inference system intended to follow a sliding-mode controller (SMC), and a robust control syste...

the present article investigates the application of high order tsk (takagi sugeno kang) fuzzy systems in modeling photo voltaic (pv) cell characteristics. a method has been introduced for training second order tsk fuzzy systems using anfis (artificial neural fuzzy inference system) training method. it is clear that higher order tsk fuzzy systems are more precise approximators while they cover n...

To enhance Patient’s safety, we need effective methods for risk management. This work aims to propose an integrated approach to risk management for a hospital system. To improve patient’s safety, we should develop flexible methods where different aspects of risk and type of information are taken into consideration. This paper proposes a fuzzy Bayesian network to model and analyze risk in the op...

Human-based quality control reduces the accuracy of this process. Also, the speed of decision making in some industries is very important. For removing these limitations in human-based quality control, in this paper, the design of an expert system for automatic and intelligent quality control is investigated. In fact, using an intelligent system, the accuracy in quality control is increased. It...

in this paper, a  fuzzy numerical procedure for solving fuzzy linear volterra integro-differential equations of the second kind under strong  generalized differentiability is designed. unlike the existing numerical methods, we do not replace the original fuzzy equation by a $2times 2$ system ofcrisp equations, that is the main difference between our method  and other numerical methods.error ana...

one of the most important challenges in nonlinear, multi-input multi-output (mimo) and time variant systems (e.g., robot manipulator) is designing a controller with acceptable performance. this paper focused on design a new artificial non linear controller with on line tunable gain applied in the robot manipulator. the sliding mode fuzzy controller (smfc) was designed as 7 rules mamdani’s infer...

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 work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...