Bayesian probability

Electrical engineering

Physics

Pure mathematics

Mathematics

Naive Bayes classifier

Algorithm

Magnetic field

Support vector machine

Machine learning

Engineering

Voltage

Artificial intelligence

Bayes' theorem

Programming language

Electrostatic discharge

Simplicity

Software

Computer science

Quantum mechanics

Field (mathematics)

Artificial neural network

Electric field

Electronics

The magnetic and electric fields of electrostatic discharges are assessed using the Naïve Bayes algorithm, a machine learning technique. Laboratory data from electrostatic discharge generators were used for the implementation of this algorithm. The applied machine learning algorithm can be used to predict the radiated field knowing the discharge current. The results of the Naïve Bayes algorithm are compared to a previous software tool derived by Artificial Neural Networks, proving its better outcome. The Naïve Bayes algorithm has excellent performance on most classification tasks, despite its simplicity, and usually is more accurate than many sophisticated methods. The proposed algorithm can be used by laboratories that conduct electrostatic discharge tests on electronic equipment. It will be a useful software tool, since they will be able to predict the radiating electromagnetic field by simply measuring the discharge current from the electrostatic discharge generators.

Machine Learning Techniques for the Prediction of the Magnetic and Electric Field of Electrostatic Discharges