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.

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