Reconstruction of 3D Permittivity Profile of a Dielectric Sample Using Artificial Neural Network Mathematical Model and FDTD Simulation

2018 ◽  
pp. 272-279
Author(s):  
Mikhail Abrosimov ◽  
Alexander Brovko ◽  
Ruslan Pakharev ◽  
Anton Pudikov ◽  
Konstantin Reznikov
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Fdtd Simulation ◽  
Dielectric Sample ◽  
Artificial Neural

scholarly journals Physicochemical Changes of the Gluten-Free Rice-Buckwheat Cookies during Storage – Artificial Neural Network Model

2019 ◽  
Author(s):  
Mladenka Pestorić ◽  
Marijana Sakač ◽  
Lato Pezo ◽  
Dubravka Škrobot ◽  
Nataša Nedeljković ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Principal Component ◽  
Physicochemical Characteristics ◽  
Storage Conditions ◽  
Gluten Free ◽  
Physicochemical Changes ◽  
Artificial Neural

The influence of storage time, temperature, and packaging on some physicochemical characteristics of gluten-free rice-buckwheat cookies was studied. Shelf life markers, such as water activity (aw), hydroxymethylfurfural (HMF), firmness, and color parameters were modelled in relation to different storage conditions. Principal component analysis was applied to study the similarity among samples according to the observed parameters. The mathematical model in the form of an artificial neural network was developed to predict the physicochemical parameters of cookies during 6-month storage. The most evident differentiation among samples was observed for color coordinate a*, aw , and HMF. Regarding the methods for determination of the parameters, priority should be given to the instrumental determination of color as the most convenient method. The processing of experimental data allowed the creation of useful mathematical model to be used in predicting the behavior of physicochemical changes of cookies by different factor combinations during storage.

Predicting Model in the Smelting Magnesium by Silicon-Thermo-Reduction Based on Artificial Neural Network

2010 ◽  
Vol 146-147 ◽  
pp. 571-574
Author(s):  
Liang Bo Ji ◽  
Yong Zhi Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Effective Means ◽  
Limited Liability ◽  
Predicting Model ◽  
Artificial Neural ◽  
Set Up

This paper described the application of neural networks in predicting the rate of producing magnesium by silicon-thermo-reduction. Fir st of all, a mathematical model between the process parameters and the the rate of producing magnesium was set up with neural network. When the model was satisfied, it could be used for predicting the rate of producing magnesium. Through doing a great number of productive tests in the winca(hebi) magnesium company with limited liability according to the satisfied model, the rate of the producing magnesium is increasing obviously. So it is a kind of effective means for increasing producing magnesium by silicon-thermo-reduction.

MATHEMATICAL-ARTIFICIAL NEURAL NETWORK HYBRID MODEL TO PREDICT ROLL FORCE DURING HOT ROLLING OF STEEL

2013 ◽  
Vol 02 (01) ◽  
pp. 1350004 ◽  
Author(s):  
S. RATH ◽  
P. P. SENGUPTA ◽  
A. P. SINGH ◽  
A. K. MARIK ◽  
P. TALUKDAR
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Flow Stress ◽  
Mathematical Models ◽  
Hybrid Model ◽  
Data Driven ◽  
Roll Force ◽  
Hot Strip ◽  
Artificial Neural

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

scholarly journals Artificial Neural Network Prediction of the Optimal Setup Parameters of a Seven Degrees of Freedom Mathematical Model of a Race Car: IndyCar Case Study

Vehicles ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 300-329
Author(s):  
Danilo D’Andrea ◽  
Giacomo Risitano ◽  
Ernesto Desiderio ◽  
Andrea Quintarelli ◽  
Dario Milone ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Degrees Of Freedom ◽  
Race Car ◽  
Quarter Car Model ◽  
Neural Network Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Time Domain

The aim of this paper is the development of a 7-DOF (Degrees Of Freedom) mathematical model of an IndyCar and the implementation of an Artificial Neural Network in order to predict the optimal setup parameters of the car, reducing time and costs for race teams. The mathematical model is created by using MATLABTM and Simulink software starting from a telemetry acquisition at the Houston circuit and is based on Vertical Vehicle Dynamic equations. The optimal setup parameters have been predicted through an Artificial Neural Network (ANN) by using the NFTOOL Toolbox of MATLABTM software. ANN is implemented in a Quarter Car model, firstly, in order to train the network and predict the parameters able to reduce tire deflection and suspension travel in the time domain and the resonance peaks amplitude in the frequency domain. Then, it is implemented in the 7-DOF model in order to predict the best setup parameters able to reduce body movements and the weight transfers of the car.

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