%0 Journal Article
%A Nezhadshahbodaghi, M.
%A Bahmani, K.
%A Mosavi, M. R.
%A MartÃn, D.
%T Chaotic Time-Series Prediction using Intelligent Methods
%J IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING
%V 19
%N 2
%U http://ijeee.iust.ac.ir/article-1-2692-en.html
%R 10.22068/IJEEE.19.2.2692
%D 2023
%K Time Series, Neural Networks, Heuristic Methods, Fuzzy Systems.,
%X 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.
%> http://ijeee.iust.ac.ir/article-1-2692-en.pdf
%P 2692-2692
%& 2692
%!
%9 Research Paper
%L A-10-78-33
%+ Department of Electrical Engineering, Iran University of Science and Technology
%G eng
%@ 1735-2827
%[ 2023