| Abstrakt: |
Mathematical models are beneficial in representing a given dataset, especially in engineering applications. Establishing a model can be used to visualise how the model fits the dataset, as was done in this research. The Levenberg–Marquardt model was proposed as a training algorithm and employed in the backpropagation algorithm or multilayer perceptron. The dataset obtained from a previous researcher consists of electrochemical data of uncoated and coated additive manufacturing steel with Ni-P at several testing periods. The model's performance was determined by regression value (R) and mean square error (MSE). It was found that the R values for non-coated additive manufacturing steel were 0.9999, 1, and 1, while MSE values were 1.14 × 10−6, 2.99 × 10−7, and 5.10 × 10−7 for 0 h, 288 h, and 572 h, respectively. Meanwhile, the R values for the Ni-P coated additive manufacturing steel were 1, 1, 1, while the MSE values were 1.06 × 10−7, 1.15 × 10−8, and 6.59 × 10−8 for 0 h, 288 h, and 572 h, respectively. The high R and low values of MSE emphasise that this training algorithm has shown good accuracy. The proposed training algorithm provides an advantage in processing time due to its ability to approach second-order training speed without having to compute the Hessian Matrix. [ABSTRACT FROM AUTHOR] |
