AN EFFECTIVE MODEL FOR CARBON DIOXIDE EMISSIONS PREDICTION: COMPARISON OF ARTIFICIAL NEURAL NETWORKS LEARNING ALGORITHMS

被引：2

作者：

Abdullah, Lazim ^{[1
]}

Pauzi, Herrini Mohd ^{[1
]}

机构：

[1] Univ Malaysia Terengganu, Sch Informat & Appl Math, Kuala Terengganu 21030, Malaysia

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS | 2014年 / 13卷 / 03期

关键词：

CO2; emissions; forecasting; artificial neural network; learning algorithm;

D O I：

10.1142/S146902681450014X

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper intends to compare various learning algorithms available for training the multi-layer perceptron (MLP) type of artificial neural networks (ANNs). By using different learning algorithms, this study investigates the performances of gradient descent (GD) algorithm; Levenberg-Marquardt (LM) algorithm; and also Boyden, Fletcher, Goldfarb and Shannon (BFGS) algorithm to predict the emissions of carbon dioxide (CO2) in Malaysia. The impact factors of emissions, such as energy use; gross domestic product per capita; population density; combustible renewable and waste; also CO2 intensity were employed in developing all ANN models investigated in this study. A wide variety of standard statistical performance evaluation measures were employed to evaluate the performances of various ANN models developed. The results obtained in this study indicate that the LM algorithm outperformed both BFGS and GD algorithms.

引用

页数：17

共 50 条

[21] Modeling of supercritical carbon dioxide extraction by artificial neural networks
Li, H
Yang, SX
Shi, J
8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING, 2001, : 337 - 342
[22] Diabetes Prediction with Supervised Learning Algorithms of Artificial Neural Network
Sapon, Muhammad Akmal
Ismail, Khadijah
Zainudin, Suehazlyn
Ping, Chew Sue
SOFTWARE AND COMPUTER APPLICATIONS, 2011, 9 : 57 - 61
[23] Comparison of nolinear system prediction by artificial neural networks
Samek, D.
ANNALS OF DAAAM FOR 2007 & PROCEEDINGS OF THE 18TH INTERNATIONAL DAAAM SYMPOSIUM: INTELLIGENT MANUFACTURING & AUTOMATION: FOCUS ON CREATIVITY, RESPONSIBILITY, AND ETHICS OF ENGINEERS, 2007, : 669 - 670
[24] Application of Artificial Neural Networks for Prediction of Learning Performances
Dharmasaroja, Permphan
Kingkaew, Nicha
2016 12TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), 2016, : 745 - 751
[25] Performance comparison of artificial neural networks learning algorithms and activation functions in predicting severity of autism
Chand Y.
Alam M.A.
Tejaswini Y.R.
Network Modeling Analysis in Health Informatics and Bioinformatics, 2015, 4 (1)
[26] Comparison of Artificial Neural Networks Algorithms on Datasets with Different Characteristics
Pilosta, Bruno
Oreski, Dijana
Kadoic, Nikola
INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 1, INTELLISYS 2023, 2024, 822 : 371 - 385
[27] The Influence of Genetic Algorithms on Learning Possibilities of Artificial Neural Networks
Kotyrba, Martin
Volna, Eva
Habiballa, Hashim
Czyz, Josef
COMPUTERS, 2022, 11 (05)
[28] Artificial neural networks - basic network structures and learning algorithms
Osowski, Stanislaw
PRZEGLAD ELEKTROTECHNICZNY, 2009, 85 (08): : 1 - 8
[29] Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction
Al-Dahidi, Sameer
Ayadi, Osama
Adeeb, Jehad
Louzazni, Mohamed
FRONTIERS IN ENERGY RESEARCH, 2019, 7
[30] Comparison the capability of artificial neural network (ANN) and EOS for prediction of solid solubilities in supercritical carbon dioxide
Lashkarbolooki, M.
Vaferi, B.
Rahimpour, M. R.
FLUID PHASE EQUILIBRIA, 2011, 308 (1-2) : 35 - 43

← 1 2 3 4 5 →