Modeling for electrical impedance spectroscopy of (4E)-2-amino-3-cyanobenzo[b]oxocin-6-one by artificial neural network

Ali, H.A.M.; Mohamed, R. A.

doi:10.1016/j.ceramint.2018.03.146

Cited by 16 publications

(4 citation statements)

References 14 publications

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“…A successful model is characterized by its ease of understanding and use, and it should accurately represent the system's behavior. Different mathematical models exist, and they are used in a wide variety of applications [4][5][6][7][8][9][10][11][12][13]. ANFIS is an intelligent technique utilized to model and predict complex systems.…”

Section: Introductionmentioning

confidence: 99%

Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

Mohamed,

Atyia

2024

Phys. Scr.

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The current research investigates the conductivity σ AC of glassy As4Ge24Te72 thin films through a combined experimental and theoretical approach. It sheds light on the complex relationship between σ AC , frequency, temperature, and film thickness. Analysis reveals that σ AC follows ω r law and exhibits hopping behavior. To effectively model and predict σ AC , an adaptive neuro-fuzzy inference system (ANFIS) is utilized. The ANFIS model successfully simulates the experimental data, showing high accuracy. Additionally, the prediction of experimentally measured values of σ AC is processed as a testing step and provides acceptable results. That enables ANFIS to enlarge the scale and complete the missing parts in the trained datasets by predictions for unmeasured σ AC values. The ANFIS network was built using MATLAB-R2017a. It consists of two inputs (frequency and temperature) and one output (AC conductivity). The precision is confirmed by calculating the modeling errors across the training process. The mean absolute errors MAE through the modeling process are not exceeding 10 − 5 . This average error value demonstrates the model’s effective ability. The alignment between experimental and ANFIS modeling results open up exciting possibilities for future research. By predicting the properties of the understudy material using ANFIS model, we can pave the way for more compact and efficient electrical devices.

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Section: Introductionmentioning

confidence: 99%

Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

Mohamed,

Atyia

2024

Phys. Scr.

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“…Artificial neural network (ANN) seems to be very promising for complex data analysis with applications to batteries, medical diagnosis, and prognosis analysis [20][21][22][23]. ANN has been used as non-linear regression model to extract patterns or recognition the trends through a learning process.…”

Section: Introductionmentioning

confidence: 99%

“…The network has been able to generate an associated data after the successful training. Regarding impedance spectroscopy, the real, imaginary parts of impedance and loss tangent have been predicted successfully for an unknown condition from inputs of frequency and temperatures for organic compound [22]. Recently, the accuracy of equivalent circuit model parameters (R, C) obtained from ANN has been examined for both leadacid and Li ion batteries [24].…”

Section: Introductionmentioning

confidence: 99%

Modeling of the impedance data of gadolinia doped ceria based actuators: a distribution function of relaxation times and machine learning approach

Paul

2020

J. Phys. D: Appl. Phys.

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Gadolinia-doped ceria is one of the most extensively examined oxide ion conductors to exhibit large nonclassical electrostriction. The electromechanical response depends on the grain and grain boundaries which can be probed using electrochemical impedance spectroscopy. In this study, we have modeled the impedance spectra from buckled and free standing gadolinia doped ceria (Gd0.2Ce0.8O1.9) membrane in Al/Ti/Gd0.2Ce0.8O1.9/Ti/Al electromechanical device, at different AC excitation voltages (2, 4, 6, 8 and 10 V), and starting from room temperature to 100 °C. The analysis of impedance spectrum is commonly done by equivalent circuit modeling to obtain the resistors ( R ) and capacitors ( C ). However, to overcome the inherent problems like non-uniqueness, over fitting, presently different approaches have been chosen such as impedance spectroscopy genetic programming (ISGP) and artificial neural networks (ANNs). ISGP finds an analytic form of the distribution function of relaxation times (DFRTs) using impedance spectra. The DFRT analysis reveals that the grain boundary capacitance for the membrane ∼10−8–10−9 F decreases with increasing temperature and excitation voltages. Besides, neural networks, which are optimized by Bayesian regularization, simulate both the real and imaginary parts of impedance following the pattern of experimental data up to several Hz. The DFRT analysis on this simulated impedance data shows an effect of grain boundaries. Indeed, ANNs as optimized by Levenberg-Marquardt method, can estimate the R and C ’s for present system with maximum relative errors of 24% and 22%, respectively.

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“…By using ANN, the complicated behaviors of different types of properties can be modeled and predicted. Many investigations have been presented on utilizing an ANN model in predicting different properties of nanofluids [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Modeling electrical properties of nanofluids using artificial neural network

Mohamed

2019

Phys. Scr.

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This paper presents a theoretical study of the electrical properties of two different samples of nanofluids (MgO and Si–TiO2 nanoparticles in ethylene glycol EG as the base fluid) using an artificial neural network (ANN) model. Experimental data were extracted from previous experimental studies and used as inputs. A learning ANN method was applied based on the back propagation technique. The optimal network structure, which produces the most acceptable performance, was attained. Electrical conductivity and permittivity in terms of nanoparticle concentration, temperature and frequency were simulated and predicted using the ANN model. A new nonlinear equation describing the behavior of electrical properties of nanofluids was obtained. The simulation results of the ANN model are highly accurate in comparison with experimental data. Predictions of values that are not involved in the experimental data range were carried out and provide excellent results. The mean squared error and regression coefficient were also determined. Their values support the success of the ANN model. The main purpose of the paper is to show the ability and power of an ANN model in estimating and predicting electrical properties of nanofluids. According to this research, the ANN model can be utilized as an efficient tool to predict the electrical properties of nanofluids. It can also achieve great links between practical and theoretical branches.

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Modeling for electrical impedance spectroscopy of (4E)-2-amino-3-cyanobenzo[b]oxocin-6-one by artificial neural network

Cited by 16 publications

References 14 publications

Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

Modeling of the impedance data of gadolinia doped ceria based actuators: a distribution function of relaxation times and machine learning approach

Modeling electrical properties of nanofluids using artificial neural network

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