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Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for

Mayra A Torres-Hernández^1,2,3, Miguel H Escobedo-Barajas^2,3, Héctor A Guerrero-Osuna²

¹Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Ingeniería Campus Zacatecas, Calle Circuito Cerro del Gato No. 202, Col. Ciudad Administrativa, Zacatecas 98160, Mexico.

Sensors (Basel, Switzerland)

|August 12, 2023

Summary

This study compared artificial intelligence (AI) software for character recognition across personal computers, cloud computing, and smart cyber-physical systems. All three achieved 97% accuracy, showing AI in fog technology is a viable option for smart systems.

Keywords:

Industry 4.0 artificial neural networks cloud computing fog computing multilayer perceptron smart cyber-physical systems

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Area of Science:

Computer Science
Artificial Intelligence
Cyber-Physical Systems

Background:

Modern society is undergoing a significant transformation driven by digitization and technologies like the Internet of Things, cloud computing, and machine learning.
These advancements are realizing the concepts of the smart factory and Industry 4.0.
The intelligence in smart cyber-physical systems is predominantly software-based, making AI software design a critical research area.

Purpose of the Study:

To study and compare the performance of a multilayer perceptron artificial neural network for character recognition.
To evaluate this AI model across three distinct implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems.

Main Methods:

Development and implementation of a multilayer perceptron artificial neural network for character recognition.
Training and testing the artificial neural network on personal computers, cloud computing platforms, and smart cyber-physical systems.
Performance evaluation based on training time and accuracy metrics.

Main Results:

The multilayer perceptron achieved a similar accuracy of 97% across all three tested technologies.
Significant differences were observed in the training times required for each implementation environment.
Artificial intelligence embedded in fog technology demonstrated promising performance for smart cyber-physical systems.

Conclusions:

The study validates the effectiveness of multilayer perceptron networks for character recognition in Industry 4.0 applications.
While accuracy was consistent, implementation technology significantly impacts AI model training duration.
Fog-based artificial intelligence presents a compelling and efficient alternative for developing advanced smart cyber-physical systems.