GENETIC ALGORITHM ARTIFICIAL NEURAL NETWORK IN NEAR INFRARED SPECTROSCOPIC QUANTIFICATION - Al-Kaf, Hasan Ali Gamal; Alduais, Nayef Abdulwahab; Al-Subari, Musaed (APTIKOM Journal on Computer Science and Information Technologies, 2018)
The implantation of a genetic algorithm (GA) in quantitating components of interest in near infraredspectroscopic analysis could improve the predictive ability of a regression model. Thus, this study investigates thefeasibility of a single layer Artificial Neuron Network (ANN) that trained with Levenberg-Marquardt (SLM) coupledwith GA in predicting the boiling point of diesel fuel and the blood hemoglobin using near infrared spectral data. Theproposed model was compared with a well-known model of Partial Least Squares (PLS) with and without GeneticAlgorithm. Results show that the proposed model achieved the best results with root mean square error of prediction(RMSEP) of 3.6734 and correlation coefficient of 0.9903 for the boiling point, and RMSEP of 0.2349 and correlationcoefficient of 0.9874 among PLS with and without GA, and SLM without GA. Findings suggest that the proposed SLMGA is insusceptible to the number of iterations when the SLM was trained with excessive iteration after the optimaliteration number. This indicates that the proposed model is capable of avoiding overfitting issue that due to excessivetraining iteration.