Chaotic Time-Series Prediction using Intelligent Methods

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.