Determination of the Relative Magnetic Permeability by Using an Adaptive Neuro-Fuzzy Inference System and 2d-Fem

Mohdeb, Naamane; Mékidèche, Mohamed Rachid

doi:10.2528/pierb10050201

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…In above equations x and y are input variables to the node i, A i , B i are fuzzy sets which are characterized by convenient MFs and finally p i , q i , and r i are the consequent parameters described in previous studies [40,45,46].…”

Section: Anfismentioning

confidence: 99%

Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes

Yaqub

Eren

Eyüpoğlu

2019

Environmental Engineering Research

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In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R 2 values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.

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Section: Anfismentioning

confidence: 99%

Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes

Yaqub

Eren

Eyüpoğlu

2019

Environmental Engineering Research

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“…In general, neuro-fuzzy system has input and output layers, and three hidden layers that represent membership functions and fuzzy rules. Each node in a layer receives input signals from a previous layer and transmits its output signals to nodes in the next layer (Mohdeb and Mekideche, 2010). In the adaptive network, the circle nodes describe fixed nodes and square nodes describe adaptive nodes.…”

Section: Adaptive Neuro-fuzzy Inference System Architecturementioning

confidence: 99%

“…Using a given input-output data set, ANFIS build a fuzzy inference system whose membership function parameters are adjusted through the learning process (Mohdeb and Mekideche, 2010). Figure (1) illustrates ANFIS architecture for Takagi-Sugeno type fuzzy inference system, where nodes of the identical layer have the same functions.…”

Section: Adaptive Neuro-fuzzy Inference System Architecturementioning

confidence: 99%

“…The parameter sets are computing according to given training data and a learning procedure for complete a desired input-output data set (Mohdeb and Mekideche, 2010 (i = 1, 2) are the coefficients of the first-order polynomial linear functions. Structure of a two-input first-order Sugeno fuzzy model with two rules is shown in Figure (1) and consists of five layers (Jang, 1993).…”

Section: Adaptive Neuro-fuzzy Inference System Architecturementioning

confidence: 99%

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Applying of an Adaptive Neuro Fuzzy Inference System for Prediction of Unsaturated Soil Hydraulic Conductivity

Al-Sulaiman¹

2015

Biosci., Biotech. Res. Asia

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The unsaturated hydraulic conductivity of soil (K u ) is one of the most principal parameters in the study of water movement in the soil. The field measurement methods of (K u ) are hard and expensive. So, indirect prediction of (K u ) has received considerable attention as published in the research papers to be an alternative approach. However, prediction models for soil hydraulic conductivity are now widely used informative tools for rapid and cost-effective assessment. Thus in this study, an attempt has been made to apply an adaptive neuro-fuzzy inference system (ANFIS) for predicting (K u ). The input variables were ECRatio (electric conductivity of water divided by electric conductivity of soil), SARRatio (sodium adsorption ratio of water divided by sodium adsorption ratio of soil), soil texture index (calculated from clay, sand and silt), suction rate, organic matter in the soil, initial soil moisture content and initial soil bulk density. The Gaussian membership function was the best for the input variables. The Hybrid learning was selected for predicting (K u ) with ANFIS. Three performance functions namely; root mean squared error (RMSE), mean error (ME) and coefficient of determination (R 2 ), were used to evaluate the predictive capability of the suggested (ANFIS). The obtained results for testing data (9 points) indicated that the R 2 values relating predicted versus measured estimates of (K u ) was 0.783, ME was found to be 0.118 cm/sec and RMSE was found to be 0.472 cm/sec. As a result, it appears that applying ANFIS suggests a new approach for determining (K u ) along with saving time and cost.

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“…The ability of soft computing techniques in modeling complicated problems in a vanishingly short time instead of using numerical or analytical approach [25][26][27][28][29][30][31][32] may provide a fast and accurate solution to this problem. Among the soft computing techniques the ability of fuzzy inference method in solving complicated electromagnetic problems such as microwave filter tuning [33,34], EMC problems [35], resonant frequency computation [36,37], determination of the transmission lines characteristic parameters [38], determination of the relative magnetic permeability [39] and also antenna modeling [40][41][42] has been proved in several publications. Artificial neural network (ANN) which is also a well-known soft computing technique has been used recently in microwave filter design [43].…”

Section: Introductionmentioning

confidence: 99%

Active Learning Method for the Determination of Coupling Factor and External Q in Microstrip Filter Design

Rezaee¹,

Tayarani²,

Knöchel³

2011

PIER

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Abstract-In the final step of any filter design process, the desired center frequency, coupling factor and external quality factor (Q ext ) are used to determine the physical parameters of the filter. Although in the most cases the physical dimensions of a single resonator for a given center frequency are determined using exact analytical or simple approximate equations, usually such simple equations cannot be found to easily relate the required coupling factor and Q ext to the physical parameters of the filter. Analytical calculation of coupling factor and Q ext versus dimensions are usually complicated due to the geometrical complexities or in some cases such as microstrip resonators due to the lack of exact solution for the field distribution. Therefore coupling factor and Q ext of various kinds of resonators, especially microstrip resonators, are related to the physical parameters of the structure by the use of time consuming full wave simulations. In this paper a surprisingly fast and completely general approach based on a soft computing pattern-based processing technique, called active learning method (ALM) is proposed to overcome the time consuming process of coupling factor and Q ext determination. At first the ALM technique and the steps of modeling are generally described, then as an example and in order to show the ability of the method this modeling approach is implemented to model the coupling factor and Q ext surfaces of microstrip open-loop resonators versus physical parameters of the structure. Using the ALM-based extracted surfaces for coupling factor and Q ext , two four pole Chebychev bandpass filters are designed and fabricated. Good agreement between the measured and simulated results validates the accuracy of the proposed approach.

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Determination of the Relative Magnetic Permeability by Using an Adaptive Neuro-Fuzzy Inference System and 2d-Fem

Cited by 3 publications

References 11 publications

Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes

Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes

Applying of an Adaptive Neuro Fuzzy Inference System for Prediction of Unsaturated Soil Hydraulic Conductivity

Active Learning Method for the Determination of Coupling Factor and External Q in Microstrip Filter Design

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