Volume 19, Issue 2 (June 2023)                   IJEEE 2023, 19(2): 2692-2692 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Nezhadshahbodaghi M, Bahmani K, Mosavi M R, Martín D. Chaotic Time-Series Prediction using Intelligent Methods. IJEEE 2023; 19 (2) :2692-2692
URL: http://ijeee.iust.ac.ir/article-1-2692-en.html
Abstract:   (601 Views)
Today, it can be said that in every field in which timely information is needed, we can use the applications of time-series prediction. In this paper, among so many chaotic systems, the Mackey-Glass and Loranz are chosen. To predict them, Multi-Layer Perceptron Neural Network (MLP NN) trained by a variety of heuristic methods are utilized such as genetic, particle swarm, ant colony, evolutionary strategy algorithms, and population-based incremental learning. Also, in addition to expressed methods, we propose two algorithms of Bio-geography-Based Optimization (BBO) and fuzzy system to predict these chaotic systems. Simulation results show that if the MLP NN is trained based on the proposed meta-heuristic algorithm of BBO, training and testing accuracy will be improved by 28.5% and 51%, respectively. Also, if the presented fuzzy system is utilized to predict the chaotic systems, it outperforms approximately by 98.5% and 91.3% in training and testing accuracy, respectively.

 
Full-Text [PDF 1354 kb]   (447 Downloads)    
Type of Study: Research Paper | Subject: Machine Learning
Received: 2022/10/24 | Revised: 2023/06/06 | Accepted: 2023/06/11

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.