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.