Designing of Automotive Vacuum Pumps - Development of Mathematical Model for Critical Parameters and Optimization using Artificial Neural Networks

2012 ◽  
Author(s):  
Nagendiran S R ◽  
Arun Subramanian ◽  
J Suresh kumar ◽  
Ramalingam Sivanantham
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Critical Parameters ◽  
Artificial Neural

scholarly journals Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Panagiotis G. Asteris ◽  
Athanasios K. Tsaris ◽  
Liborio Cavaleri ◽  
Constantinos C. Repapis ◽  
Angeliki Papalou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fundamental Period ◽  
Critical Parameters ◽  
Frame Structures ◽  
Rc Frame ◽  
Infill Walls ◽  
Rc Structures ◽  
Artificial Neural ◽  
Rc Frame Structures

The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

scholarly journals Development of turning process digital twin based on machine learning

2021 ◽  
pp. 32-41
Author(s):  
D. A. Rastorguev ◽  
◽  
A. A. Sevastyanov ◽  
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Information Technologies ◽  
Cutting Process ◽  
Turning Process ◽  
Force Load ◽  
Cnc Machine ◽  
Digital Twin ◽  
Artificial Neural

Today, manufacturing technologies are developing within the Industry 4.0 concept, which is the information technologies introduction in manufacturing. One of the most promising digital technologies finding more and more application in manufacturing is a digital twin. A digital twin is an ensemble of mathematical models of technological process, which exchanges information with its physical prototype in real-time. The paper considers an example of the formation of several interconnected predictive modules, which are a part of the structure of the turning process digital twin and designed to predict the quality of processing, the chip formation nature, and the cutting force. The authors carried out a three-factor experiment on the hard turning of 105WCr6 steel hardened to 55 HRC. Used an example of the conducted experiment, the authors described the process of development of the digital twin diagnostic module based on artificial neural networks. When developing a mathematical model for predicting and diagnosing the cutting process, the authors revealed higher accuracy, adaptability, and versatility of artificial neural networks. The developed mathematical model of online diagnostics of the cutting process for determining the surface quality and chip type during processing uses the actual value of the cutting depth determined indirectly by the force load on the drive. In this case, the model uses only the signals of the sensors included in the diagnostic subsystem on the CNC machine. As an informative feature reflecting the force load on the machine’s main motion drive, the authors selected the value of the energy of the current signal of the spindle drive motor. The study identified that the development of a digital twin is possible due to the development of additional modules predicting the accuracy of dimensions, geometric profile, tool wear.

scholarly journals Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting

2020 ◽  
Vol 161 ◽  
pp. 878-892
Author(s):  
V.N. Sewdien ◽  
R. Preece ◽  
J.L. Rueda Torres ◽  
E. Rakhshani ◽  
M. van der Meijden
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Critical Parameters ◽  
Wind Generation ◽  
Short Term ◽  
Artificial Neural

Autonomous Design of Artificial Neural Networks by Neurex

1996 ◽  
Vol 8 (8) ◽  
pp. 1767-1786 ◽  
Author(s):  
François Michaud ◽  
Ruben Gonzalez Rubio
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Expert System ◽  
Design Process ◽  
Design Rules ◽  
Learning Abilities ◽  
Artificial Neural ◽  
Number Of Iterations ◽  
Learning Law

Artificial neural networks (ANN) have been demonstrated to be increasingly more useful for complex problems difficult to solve with conventional methods. With their learning abilities, they avoid having to develop a mathematical model or acquiring the appropriate knowledge to solve a task. The difficulty now lies in the ANN design process. A lot of choices must be made to design an ANN, and there are no available design rules to make these choices directly for a particular problem. Therefore, the design of an ANN demands a certain number of iterations, mainly guided by the expertise and the intuition of the developer. To automate the ANN design process, we have developed Neurex, composed of an expert system and an ANN simulator. Neurex autonomously guides the iterative ANN design process. Its structure tries to reproduce the design steps done by a human expert in conceiving an ANN. As a whole, the Neurex structure serves as a framework to implement this expertise for different learning paradigms. This article presents the system's general characteristics and its use in designing ANN using the standard backpropagation learning law.

scholarly journals Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Al Haidan ◽  
Osama Abu-Hammad ◽  
Najla Dar-Odeh
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Tooth Surface ◽  
Output Data ◽  
Surface Loss ◽  
Artificial Neural ◽  
High Degree

Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

scholarly journals Mathematical model for the evaluation of risk of emergency situations at a dangerous technical object based on artificial neural networks

2018 ◽  
Vol 44 ◽  
pp. 00069
Author(s):  
Nikolay Peganov ◽  
Aleksandr Tumanov ◽  
Vladimir Tumanov
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Risk Assessment ◽  
Artificial Neural Networks ◽  
Fire Risk ◽  
Management Decision ◽  
Technical Object ◽  
Emergency Situations ◽  
Artificial Neural ◽  
The Mathematical Model

In the work performed adaptation of artificial neural networks in modern security systems potentially dangerous technical objects — high-rise buildings as tools for assessing and forecasting in management decision. The study obtained the main scientific results: the mathematical model of risk assessment of man-made emergencies based on artificial neural networks; the mathematical model, adapted to the cumulative model of development technogene emergency-fire; provided risk assessment technique manmade emergencies based on artificial neural networks; represented private man-made fire risk assessment methodology using artificial neural networks.

scholarly journals Modeling of photovoltaic modules using a gray-box neural network approach

2017 ◽  
Vol 21 (6 Part B) ◽  
pp. 2837-2850
Author(s):  
Aleksandar Rankovic ◽  
Dragan Cetenovic
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Real Life ◽  
Box Model ◽  
Photovoltaic Module ◽  
Neural Network Approach ◽  
Photo Voltaic ◽  
Artificial Neural ◽  
Input Variables

This paper proposes a gray-box approach to modeling and simulation of photo-voltaic modules. The process of building a gray-box model is split into two components (known, and unknown or partially unknown). The former is based on physical principles while the latter relies on functional approximator and data-based modeling. In this paper, artificial neural networks were used to construct the functional approximator. Compared to the standard mathematical model of photovoltaic module which involves the three input variables - solar irradiance, ambient temperature, and wind speed- a gray-box model allows the use of additional input environmental variables, such as wind direction, atmospheric pressure, and humidity. In order to improve the accuracy of the gray-box model, we have proposed two criteria for the classification of the daily input/output data whereby the former determines the season while the latter classifies days into sunny and cloudy. The accuracy of this model is verified on the real-life photo-voltaic generator, by comparing with single-diode mathematical model and artificial neural networks model towards measured output power data.

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