E3S Web Conf.
Volume 287, 2021International Conference on Process Engineering and Advanced Materials 2020 (ICPEAM2020)
|Number of page(s)||6|
|Section||Process Systems Engineering & Optimization|
|Published online||06 July 2021|
A View of Artificial Neural Network Models in Different Application Areas
1 Department of Mechanical Engineering, Einstein College of Engineering, Tirunelvelli, Tamilnadu, India
2 Department of Mechanical Engineering, Raja Rajeshwari College of Engineering, Bengaluru, Karnataka, India
3 Department of Electronics and Communication Engineering, Einstein College of Engineering, Tirunelvelli, Tamilnadu, India
Neural network is a web of million numbers of inter-connected neurons which executes parallel processing. An Artificial neural network is a nonlinear mapping structure; an information processing pattern is stimulated by the approach as biological nervous system (brain) process the information. It is used as a powerful tool for modeling the data in the application domains where incomplete understanding of the data relationship to be solved with the readily available trained data. The basic element for this processing pattern is the structure of the data which is the collection of densely interconnected neurons to elucidate the problems. A prominent part of these network is their adaptive nature to “learn by example” just like human substitutes “programming” in resolving the problems. Through learning process, neural net is designed for data classification and prediction where statistical techniques and regression model have been employed. This report is an overview of artificial neural networks in different application areas and it also illustrate the architecture structure formed for the applications. It also provides information about the training algorithm used for certain application.
Key words: Artificial Neural Network and applications / data classification and prediction
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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