Open AccessGenetic algorithm artificial neural network in near infrared spectroscopic quantification
Author(s) -
Hasan Ali Gamal Al-Kaf,
Nayef Abdulwahab Mohammed Alduais,
Musaed Al-Subari
Publication year - 2020
Publication title -
aptikom journal on computer science and information technologies
Language(s) - English
Resource type - Journals
eISSN - 2528-2425
pISSN - 2528-2417
DOI - 10.34306/csit.v3i3.79
Subject(s) - overfitting , artificial neural network , correlation coefficient , mean squared error , genetic algorithm , algorithm , convergence (economics) , computer science , artificial intelligence , boiling point , mathematics , statistics , machine learning , engineering , chemical engineering , economics , economic growth
The implantation of a genetic algorithm (GA) in quantitating components of interest in near infrared spectroscopic analysis could improve the predictive ability of a regression model. Thus, this study investigates the feasibility of a single layer Artificial Neuron Network (ANN) that trained with Levenberg-Marquardt (SLM) coupled with GA in predicting the boiling point of diesel fuel and the blood hemoglobin using near infrared spectral data. The proposed model was compared with a well-known model of Partial Least Squares (PLS) with and without Genetic Algorithm. 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 correlation coefficient 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 optimal iteration number. This indicates that the proposed model is capable of avoiding overfitting issue that due to excessive training iteration.