Neural network design
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
A bootstrap evaluation of the effect of data splitting on financial time series
IEEE Transactions on Neural Networks
A comparison between neural-network forecasting techniques-case study: river flow forecasting
IEEE Transactions on Neural Networks
Discrete-time backpropagation for training synaptic delay-based artificial neural networks
IEEE Transactions on Neural Networks
Learning polynomial feedforward neural networks by genetic programming and backpropagation
IEEE Transactions on Neural Networks
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In this paper, we present a novel approach for constructing a nonlinear recursive predictor. Given a limited time series data set, our goal is to develop a predictor that is capable of providing reliable long-term forecasting. The approach is based on the use of an artificial neural network and we propose a combination of network architecture, training algorithm, and special procedures for scaling and initializing the weight coefficients. For time series arising from nonlinear dynamical systems, the power of the proposed predictor has been successfully demonstrated by testing on data sets obtained from numerical simulations and actual experiments.