Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm

Muhammad Aznil, Ab Aziz and Mohammad Fadhil, Abas and Muhamad Abdul Hasib, Ali and Norhafidzah, Mohd Saad and Mohd Hisyam, Ariff and Mohamad Khairul Anwar, Abu Bashrin (2023) Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm. In: 2023 19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023 - Conference Proceedings , 3-4 March 2023 , Kedah. pp. 207-212.. ISBN 978-166547692-8

Pdf
Estimating the Un-sampled pH Value via neighbouring points.pdf
Restricted to Repository staff only
Download (682kB) | Request a copy

Preview

Pdf
Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm_ABS.pdf
Download (271kB) | Preview

DOI/Official URL: https://doi.org/10.1109/CSPA57446.2023.10087388

Abstract

This study shows a new method to estimate unsampled pH value by utilizing neighboring pH, which according to recent literature, has not been done yet. In investigating this method, three algorithms are used: Neural Network-Genetic Algorithm (MLNN-GA), MLNN with backpropagation (MLNN-BP), and averaging method. MLNNGA and MLNN-BP are inputted with four pH values from distant adjacent locations on a similar basin. MLNN-GA and MLNN-BP utilize GA and backpropagation respectively to update the weight. GA optimizer is used in MLNN-GA where the result of each learning weight will be the initial weight of the next learning process. All three methods are compared based on RMSE, MSE and MAPE. MLNN-GA yielded the lowest average RMSE =0.026265, average MSE =0.000886 and average MAPE =0.003985 compared to MLNN-BP (average RMSE =0.042644, average MSE =0.002648, average MAPE =0.006862) and averaging method (average RMSE =0.136629, average MSE = 0.026128, average MAPE =0.150400). Noticeably, estimating unsampled pH value utilizing neighboring pH by using MLNNGA shows a better performance than MLNN-BP and averaging method.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Fitness function; Genetic algorithm; Multi-layer neural network; pH estimation; Root mean square error
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Institute of Postgraduate Studies College of Engineering Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	06 Nov 2023 06:53
Last Modified:	06 Nov 2023 06:53
URI:	http://umpir.ump.edu.my/id/eprint/38779
Download Statistic:	View Download Statistics

Actions (login required)

View Item