This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-
prediction of chaotic time series, it estimates the proper parameters of phase space …

The prediction of future values of a time series generated by a chaotic dynamical system is a
challenging task. Recently, the use of recurrent neural networks (RNN) models appears. An …

A new proposed method, ie the recurrent neural network (RNN), is introduced to predict
chaotic time series. The effectiveness of using RNN for making one-step and multi-step …

Chaos limits predictability so that the long-term prediction of chaotic time series is very
difficult. The main purpose of this paper is to study a new methodology to model and predict …

We propose a new technique of using the least squares support vector machines (LS-SVMs)
for making one-step and multi-step prediction of chaotic time series. The LS-SVM achieves …

To improve the prediction accuracy of chaotic time series, deep extraction of the system
evolutionary patterns is a key problem in modeling. In this paper, we propose a deep …

A fast evolutionary programming (FEP) is proposed to train multi-layer perceptrons (MLP) for
noisy chaotic time series modeling and predictions. This FEP, which uses a Cauchy …

Chaotic time series prediction has important research and application value, and neural
network-based prediction methods have problems such as low accuracy and difficulty in …

Radial basis function (RBF) neural network has very good performance on prediction of
chaotic time series, but the precision of prediction is great affected by embedding dimension …

A self‐constructing fuzzy neural network (SCFNN) has been successfully used for chaotic
time series prediction in the literature. In this paper, we propose the strategy of adding a …