in this study the dissolved air flotation (daf) system in oil refinery was investigated for the treatment of refinery wastewater. in order to investigate sytem a labratory scale rig was built. the aim is to remove some of the wastewater pollutant materials and data modeling of cod test.the effect of several parameters on flotation efficiency namely, saturator pressure, and coagulant dose, on co...

OBJECTIVES To develop a neuro-fuzzy system to predict the presence of prostate cancer. Neuro-fuzzy systems harness the power of two paradigms: fuzzy logic and artificial neural networks. We compared the predictive accuracy of our neuro-fuzzy system with that obtained by total prostate-specific antigen (tPSA) and percent free PSA (%fPSA). METHODS The data from 1030 men (both outpatients and ho...

in this research, pomegranate arils are dehydrated by osmotic dehydration in 40, 50, and 60 % sucrose ‎solutions and at 45, 55 and 65 degrees c‎‏ ‏‎ and weight reduction, solids grain and water loss of the products ‎were measured at 60, 120 and 180 minutes of process. osmotic dehydration processes was modeled by ‎combination of neural network and fuzzy logic techniques (neuro-fuzzy) and respons...

The interest in neuro{fuzzy systems has grown tremendously over the last few years. First approaches concentrated mainly on neuro{fuzzy controllers, whereas newer approaches can also be found in the domain of data analysis. After successful applications in Japan neuro{fuzzy concepts also nd their way into the European industries, though mainly simple models, like FAMs, still prevail. This paper...

A neuro-fuzzy network approach is developed to model the nonlinear behavior of submicron metal-oxide semiconductor field-effect transistors (MOSFETs). The proposed model is trained and implemented as a MOSFET in a software environment. The training data are obtained through various simulations of a MOSFET Berkeley short channel insulated-gate field-effect transistor model 3 (BSIM3) in HSPICE, a...

In this paper, we propose a neuro-fuzzy modeling framework to discover fuzzy rules and its application to predict chemical properties of ashes produced by thermo-electric generators. The framework is defined by several sequential steps in order to obtain a good predictive accuracy and the readability of the discovered fuzzy rules. First, a feature selection procedure is applied to the available...

Load sensor is developed using fuzzy logic as well as neuro-fuzzy method. It is two inputs and one output sensor. Both fuzzy logic and neuro-fuzzy algorithms are simulated using MATLAB fuzzy logic toolbox. This paper outlines the basic difference between the results of fuzzy logic and neuro-fuzzy algorithms and provides the better algorithm for load sensor. Index Terms —fuzzy logic, load sensor...

Pressure–volume–temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mi...

In this paper, the robust neuro-fuzzy networks (RNFNs) are proposed to improve the problems of neuro-fuzzy networks (NFNs) for modeling with outliers. Firstly, the support vector regression (SVR) approach is applied to obtain the initial structure of RNFNs. Because of the SVR approach is equivalent to solving a linear constrained quadratic programming problem under the fixed structure of SVR, t...

Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency,...