Computational Intelligence Based Chaotic Time Series Prediction Using Evolved Neural Network

Abstract

A nonlinear behavior may exist intrinsically with a deterministic dynamic system which shows high sensitivity with initial condition. Such behavior is characterized as chaotic behavior. Strange attractor confines the dynamic behavior of chaotic system in a finite state space region, and the available state variables show the stochastic behavior with time. In this research work, time delay neural network has applied to predict the various chaotic time series. Adaptive social behavior optimization has applied to evolve the optimal set of weights of the time delay neural network. Comparison of learning performance has given with popularly gradient descent-based learning. Performance evaluation has defined in terms of coefficient of determination along with root-mean-square error in prediction under learning and test phase of chaotic time series. The three benchmarks of chaotic time series (logistic differential equation, Mackey–Glass, and Lorenz system) have taken for predicting purpose. We have shown with experimental results that the proposed new method of neural network learning is very efficient and has delivered the better prediction for various complex chaotic time series.