en Machine Learning Machine Learning Research Paper Research Paper <div style="text-align: justify;"><span style="font-size:14px;"><span style="font-family:Times New Roman;"><span style="color:#000000;"><span style="line-height:115%">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.</span></span></span></span><br> <br> &nbsp;</div> Time Series, Neural Networks, Heuristic Methods, Fuzzy Systems. 2692 2692 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-78-33&slc_lang=en&sid=1 M. Nezhadshahbodaghi m_nezhadshahbodaghi@elec.iust.ac.ir 1800319475328460012079 1800319475328460012079 No Department of Electrical Engineering, Iran University of Science and Technology K. Bahmani k_bahmani@elec.iust.ac.ir 1800319475328460012080 1800319475328460012080 No Department of Electrical Engineering, Iran University of Science and Technology M. R. Mosavi m_mosavi@iust.ac.ir 1800319475328460012081 1800319475328460012081 Yes Department of Electrical Engineering, Iran University of Science and Technology D. Martín diego.martin.de.andres@upm.es 1800319475328460012082 1800319475328460012082 No ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain