scholarly journals Thermal Conductivity Modeling of Propylene Glycol - Based Nanofluid Using Artificial Neural Network

2018 ◽  
Vol 14 (1) ◽  
pp. 5281-5291 ◽  
Author(s):  
R. A. Mohamed ◽  
D. M. Habashy
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Propylene Glycol ◽  
Network Model ◽  
Neural Network Model ◽  
Network System ◽  
The Neural Network ◽  
Neural Network System

The article introduces artificial neural network model that simulates and predicts thermal conductivity and particle size of propylene glycol - based nanofluids containing Al2O3 and TiO2 nanoparticles in a temperature rang 20 - 80oc. The experimental data indicated that the nanofluids have excellent stability over the temperature scale of interest and thermal conductivity enhancement for both nanofluid samples. The neural network system was trained on the available experimental data. The system was designed to find the optimal network that has the best training performance. The nonlinear equations which represent the relation between the inputs and output were obtained. The results of neural network model and the theoretical models of the proposed system were performed and compared with the experimental results. The neural network system appears to yield the best fit consistent with experimental data. The results of the paper demonstrate the ability of neural network model as an excellent computational tool in nanofluid field.

scholarly journals Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongyan Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dynamic Characteristics ◽  
Bp Neural Network ◽  
Data Encryption ◽  
Network System ◽  
Biological Neural Network ◽  
The Neural Network ◽  
Neural Network System

Biological neural network system is a complex nonlinear dynamic system, and research on its dynamics is an important topic at home and abroad. This paper briefly introduces the dynamic characteristics and influencing factors of the neural network system, including the effects of time delay and noise on neural network synchronization, synchronous transition, and stochastic resonance, and introduces the modeling of the neural network system. There are irregular mixing problems in the complex biological neural network system. The BP neural network algorithm can be used to solve more complex dynamic behaviors and can optimize the global search. In order to ensure that the neural network increases the biological characteristics, this paper adjusts the parameters of the BP neural network to receive EEG signals in different states. It can simulate different frequencies and types of brain waves, and it can also carry out a variety of simulations during the operation of the system. Finally, the experimental analysis shows that the complex biological neural network model proposed in this paper has good dynamic characteristics, and the application of this algorithm to data information processing, data encryption, and many other aspects has a bright prospect.

Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data

2018 ◽  
Vol 233 (7) ◽  
pp. 2549-2569
Author(s):  
Farrukh Mazhar ◽  
Mohammad A Choudhry ◽  
Muhammad Shehryar
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Flight Test ◽  
Aerial Vehicle ◽  
Auto Regressive ◽  
Artificial Neural ◽  
Trained Neural Network

Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.

scholarly journals PERANCANGAN MODEL UNTUK PERAMALAN TINGKAT PERTUMBUHAN PEMBIAYAAN MELALUI METODE JARINGAN SARAF TIRUAN DI BNI SYARIAH

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Orfyanny S Themba ◽  
Susianah Mokhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Data Bank ◽  
Forecasting Accuracy ◽  
The Neural Network ◽  
Forecasting Method ◽  
Artificial Neural

ABSTRAKTren perkembangan pembiayaan di Indonesia mulai meningkat namun cenderung melambat dari tahun ke tahun. Peramalan pertumbuhan pembiayaan pada bank syariah menjadi hal yang menarik karena naik turunnya pembiayaan akan berdampak pada perekonomian Indonesia. Tujuan dari penelitian ini melakukan peramalan pertumbuhan pembiayaan dalam jangka waktu setahun melalui metode Jaringan Saraf Tiruan pada data Bank BNI Syariah dari tahun 2015 sampai dengan 2019. Hasil dari peramalan diharapkan memberi informasi bagi bank untuk menunjang pengambilan keputusan dan menyiapkan strategi meningkatkan pembiayaan sehingga semakin besar laba yang akan diperoleh. Model peramalan dibuat berdasarkan metode peramalan dan ditujukan untuk digunakan pada aplikasi peramalan pembiayaan. Model Jaringan Saraf Tiruan memiliki nilai akurasi peramalan yang tinggi karena memiliki nilai error RMSE, MAPE yang minimum. Dari hasil peramalan menggunakan model Jaringan Saraf Tiruan menunjukkan terjadi peningkatan pembiayaan pada setiap bulannya untuk akad murabahah, mudharabah, musyarakah dan qardh. Hanya pembiayaan yang menggunakan ijarah yang mengalami penurunan drastis dibanding tahun-tahun sebelumnya. Pembiayaan murabahah masih tetap mendominasi dibanding akad mudharabah, musyarakah, qardh dan ijarah selama tahun 2020 Kata Kunci: Jaringan Saraf Tiruan ;PembiayaanABSTRACT Trend of financing development in Indonesia is starting to increase but tends to slow down from year to year. It is interesting to forecast the growth of financing in Islamic banks because the up and down of financing will have an impact on the Indonesian economy. The purpose of this study to forecast financing growth within a year through the Neural Network method on BNI Syariah Bank data from 2015 to 2019. The results of the forecast are expected to provide information for banks to support decision making and prepare strategies to increase financing so that greater profits that will be obtained. The forecasting model is made based on the forecasting method and is intended for use in financing forecasting applications. The Artificial Neural Network Model has a high value of forecasting accuracy because it has a minimum error value of RMSE, MAPE. The results of forecasting using the Artificial Neural Network model show an increase in financing every month for murabahah, mudharabah, musyarakah and qardh contracts. Only financing using ijarah has experienced a drastic decline compared to previous years. Murabahah financing still dominates over the mudharabah, musyarakah, qardh and ijarah contracts during 2020Keyword: Arificial Neural Network ;Financing

scholarly journals Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5805
Author(s):  
Tianfu Ai ◽  
Bin Xu ◽  
Changle Xiang ◽  
Wei Fan ◽  
Yibo Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Simulation Software ◽  
Ducted Fan ◽  
The Neural Network ◽  
Aerial Robot ◽  
Artificial Neural ◽  
Comprehensive Modeling

A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.

scholarly journals Neural network method as means of processing experimental data on grain crop yields

2020 ◽  
Vol 161 ◽  
pp. 01031
Author(s):  
Aleksandr Nikiforov ◽  
Aleksei Kuchumov ◽  
Sergei Terentev ◽  
Inessa Karamulina ◽  
Iraida Romanova ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Crop Yields ◽  
Winter Triticale ◽  
The Neural Network ◽  
Grain Crop ◽  
Scientific Results

In the work based on agroecological and technological testing of varieties of grain crops of domestic and foreign breeding, winter triticale in particular, conducted on the experimental field of the Smolensk State Agricultural Academy between 2015 and 2019, we present the methodology and results of processing the experimental data used for constructing the neural network model. Neural networks are applicable for solving tasks that are difficult for computers of traditional design and humans alike. Those are processing large volumes of experimental data, automation of image recognition, approximation of functions and prognosis. Neural networks include analyzing subject areas and weight coefficients of neurons, detecting conflict samples and outliers, normalizing data, determining the number of samples required for teaching a neural network and increasing the learning quality when their number is insufficient, as well as selecting the neural network type and decomposition based on the number of input neurons. We consider the technology of initial data processing and selecting the optimal neural network structure that allows to significantly reduce modeling errors in comparison with neural networks created with unprepared source data. Our accumulated experience of working with neural networks has demonstrated encouraging results, which indicates the prospects of this area, especially when describing processes with large amounts of variables. In order to verify the resulting neural network model, we have carried out a computational experiment, which showed the possibility of applying scientific results in practice.

Artificial Neural Network Applied to Predicting the Surface Tension of Acoustically Levitated Droplets of Supercooling Nanofluids

NANO ◽  
2021 ◽  
pp. 2150108
Author(s):  
Baohui Wu ◽  
Yudong LIU ◽  
Dengshi Wang ◽  
Nan Jiang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Surface Tension ◽  
Artificial Neural Network ◽  
Graphene Oxide ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Deionized Water ◽  
Artificial Neural

Droplet oscillation method is a noncontact experimental approach, which can be used to measure the surface tension of acoustically levitated droplet. In this paper, we obtained huge amounts of experimental data of deionized water and water-based graphene oxide nanofluids within the temperature range of [Formula: see text]8.2–[Formula: see text]C. Based on the experimental data, we analyzed the influence of droplet’s deformation and frequency shift phenomenon on the surface tension of levitated droplet. Eight parameters that strongly correlate with surface tension were found and used as input neurons of artificial neural network model to predict the surface tension of supercooling graphene oxide nanofluids. The experimental data of nonsupercooling graphene oxide nanofluids were used as training set to optimize artificial neural network model, and that of deionized water were served as validation set, which was used to verify the predictive ability of artificial neural network model. The root mean square error of the optimized artificial neural network model to validation set is only 0.2558[Formula: see text]mN/m, and the prediction values of the surface tension of supercooling deionized water were in good agreement with the theoretical values calculated by Vargaftik equation, which indicates that artificial neural network model can deal well with the complex nonlinear relationship. Afterwards, we successfully predicted the surface tension of supercooling nanofluids by means of the optimized artificial neural network model and obviously reduced the dispersion and deviation caused by droplet deformation and other problems during oscillation process.

scholarly journals Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen

2021 ◽  
Vol 11 (22) ◽  
pp. 10834
Author(s):  
Seok Yoon ◽  
Dinh-Viet Le ◽  
Gyu-Hyun Go
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Parametric Study ◽  
Mechanical Analysis ◽  
Frozen Soil ◽  
Frost Heave ◽  
The Neural Network ◽  
Artificial Neural

Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R2 = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.

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