1. SISN
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  3. Volume 15, 2024
  4. Evaluation of Classification Accuracy Using Feedforward Neural Network for Dynamic Objects

Evaluation of Classification Accuracy Using Feedforward Neural Network for Dynamic Objects

SISN.
2024;
Volume 15
: pp. 260 - 272
https://doi.org/10.23939/sisn2024.15.260
Authors:
  1. Ivan Peleshchak
  2. Diana Koshtura
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University, Information Systems and Networks Department

Abstract. This paper investigates the impact of the number of hidden layers, the number of neurons in these layers, and the types of activation functions on the accuracy of classifying projectiles of six types (A – (artillery); A/M – (artillery/missile); A/R – (armor-piercing); A/RC – (armor-piercing- incendiary); M – (missile); R – (armor-piercing shells)) using a multi-layer neural network, evaluated by a confusion matrix. Specifically, confusion matrices were constructed to assess the accuracy of classifying projectiles of six types (A – (artillery); A/M – (artillery/missile); A/R – (armor-piercing), ; A/RC – (armor-piercing-incendiary); M – (missile); R – (armor-piercing shells)) using a multi-layer perceptron with one, two, and three hidden layers and activation functions: Logistic, Tanh, Relu, Softmax, respectively. It was found that the highest accuracy in classifying projectiles is achieved using a neural network with two hidden layers, with 33 neurons in the first hidden layer with Tanh activation function and 8 neurons with Tanh activation function in the second hidden layer, and Softmax for the neurons in the output layer.

confusion matrix
projectile classification
neural network
computational optimization
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