Performance of Levenberg-Marquardt Algorithm in Backpropagation Network Based on the Number of Neurons in Hidden Layers and Learning Rate

Abstract

One of the supervised learning paradigms in artificial neural networks (ANN) that are in great developed is the backpropagation model. Backpropagation is a perceptron learning algorithm with many layers to change weights connected to neurons in hidden layers. The performance of the algorithm is influenced by several network parameters including the number of neurons in the input layer, the maximum epoch used, learning rate (lr) value, the hidden layer configuration, and the resulting error (MSE). Some of the tests conducted in previous studies obtained information that the Levenberg-Marquardt training algorithm has better performance than other algorithms in the backpropagation network, which produces the smallest average error with a test level of α = 5% which used 10 neurons in a hidden layer. The number of neurons in hidden layers varies depending on the number of neurons in the input layer. In this study an analysis of the performance of the Levenberg-Marquardt training algorithm was carried out with 5 neurons in the input layer, a number of n neurons in hidden layers (n = 2, 4, 5, 7, 9), and 1 neuron in the output layer. Performance analysis is based on network-generated errors. This study uses a mixed method, namely development research with quantitative and qualitative testing using ANOVA statistical tests. Based on the analysis, the Levenberg-Marquardt training algorithm produces the smallest error of 0.00014 ± 0.00018 on 9 neurons in hidden layers with lr = 0.5. Keywords: hidden layer, backpropogation, MSE, learning rate, Levenberg-Marquardt.