The objective of this work is to model simulation data a dust devils in Comsol using neuro-fuzzy methods (ANFIS: Adaptive Neuro Fuzzy Inference Systems) and perceptron neural networks. Since the number simulations performed was insufficient, we used Spline function increase amount data. results show that more effective than obtained models are excellent, with Nash -Sutcliffe criterion value abo...

in this study, application of adaptive neuro-fuzzy inference system (anfis) in forecasting three perspectives (1, 2, and 4 years) ahead of iran’s agricultural products export was compared with arima as the most common econometrics linear forecasting method. for this purpose, iran’s agricultural products export revenues related to 1959-2010, and forecast performance measures such as r2, mad, and...

In this paper, an adaptive neuro fuzzy sliding mode based genetic algorithm (ANFSGA) controlsystem is proposed for a pH neutralization system. In pH reactors, determination and control of pH isa common problem concerning chemical-based industrial processes due to the non-linearity observedin the titration curve. An ANFSGA control system is designed to overcome the complexity of precisecontrol o...

In the backward metal flow forming process, mean thickness of formed tubes is crucial in determining quality. Consequently, predicting incremental and correlating these values with parameters can be useful to control this vital target. Accordingly, four different techniques adaptive neuro-fuzzy inference system (ANFIS) used predict parts produced by process. The objective determine best members...

In statistics, signal processing, and mathematical finance; a time series is a sequence of data points that measured at uniform time intervals. The prediction of time series is a very complicated process. In this paper, an improved Adaptive Neuro Fuzzy Inference System (ANFIS) is taken for predicting Mackey-Glass which is one of the chaotic time series. In the modeling of linear and stationary ...

Precise control of electro-hydraulic actuator (EHA) system has been an interesting subject due to its nonlinearities and uncertainties characteristics. Good control can be designed when precise model of the system is available. Linear ARX modelling has widely been applied and satisfying result has been obtained, through linearization process. The objective of this paper is to compare ARX model ...

This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates wit...

This paper presents a methodology for the detection of broken rotor bar fault and power quality (PQ) disturbances in the supply to induction motor. These faults lead to increase in the temperature and decrease in the efficiency of the induction motor. Wavelet transform is applied to the stator current, for the extraction of the signature of the fault. These wavelet coefficients are fed as input...

The purpose of this study is to model and optimize the detection of tar in cigarettes during the manufacturing process and show that low yield cigarettes contain similar levels of nicotine as compared to high yield cigarettes while B (Benzene), T(toluene) and X (xylene) (BTX) levels increase with increasing tar yields. A neuro-fuzzy system which comprises a fuzzy inference structure is used to ...

Trust prediction turns out to be an important challenge when cooperation among intelligent agents with an impression of trust in their mind, is investigated. In other words, predicting trust values for future time slots help partners to identify the probability of continuing a relationship. Another important case to be considered is the context of trust, i.e. the services and business commitmen...