In this paper, an approach is introduced which permits a model-free identification and prediction of time-dependent structural behavior. The numerical approach is based on recurrent neural networks for uncertain data. Time-dependent results obtained from measurements or numerical analysis are used to identify the uncertain long-term behavior of engineering structures. Thereby, the uncertainty o...

Recurrent fuzzy neural networks (FNNs) have been widely applied to dynamic system processing problems. However, most recurrent FNNs focus on the use of type-1 fuzzy sets. This paper proposes a Mamdani-type recurrent interval type-2 FNN (M-RIT2FNN) that uses interval type-2 fuzzy sets in both rule antecedent and consequent parts. The reason for using interval type-2 fuzzy sets is to increase net...

This paper presents a continuous facility location model with fuzzy methodology. The developments concern mainly to some drawbacks in the initial model which takes it far from being used in practice. A fuzzy modeling method is proposed to estimate the required functions in the initial model. Structure identification in the proposed fuzzy modeling method is carried out using subtractive clusteri...

The composition and principle of an electrohydraulic servo oil film stiffness control system of hydrostatic center frame will be introduced, and the system model identification as well as the verification will be carried out based on semi-physical simulation environment of Real-time Workshop (RTW) and system identification toolbox in MATLAB. A control strategy of fuzzy sliding model variable st...

This paper presents a novel approach to nonlinear black-box system identification which combines Qualitative Reasoning (QR) methods with fuzzy logic systems. Such a method aims at building a good initialization of a fuzzy identifier, so that it will converge to the inputoutput relation which captures the nonlinear dynamics of the system. Fuzzy inference procedures should be initialized with a r...

This paper presents an approach to build a soft sensor based on computational intelligence techniques. The goal is to identify fuzzy models from numerical data. First of all, the fuzzy model input variables are selected from the secondary variables set by applying Kohonen maps. Then, the Lipschitz quotients are used to select the lag structure of the fuzzy model. A fuzzy clustering algorithm is...

While the heights and numbers of tires in the assembly line are changing, the traditional methods are difficult to identify the types of the tires, so an indirect tire identification method is proposed to identify the types of the tires, which uses the widths of the tires, the diameters and the shapes of the hubs to classify the tires. First, the tires are identified according to the widths and...

in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...

--This paper is a brief review of fuzzy vault which is a biometric template security technique. The advent of technology over the last decade has established biometric identification as an electronic equivalent to physical verification. Biometric traits offer a reliable solution to the problem of user authentication in identity management systems. Biometric based authentication has more advanta...

A new method called cuckoo search (CS) is used to extract and learn the Takagi-Sugeno (T-S) fuzzy model. In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T-S fuzzy model. These parameters are learned simultaneously. The optimized T-S fuzzy model is validated by using t...