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Moller, M.F. (1993) A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Networks, 6, 525-533.
https://doi.org/10.1016/S0893-6080(05)80056-5

has been cited by the following article:

• TITLE: A Nonlinear Autoregressive Scheme for Time Series Prediction via Artificial Neural Networks

  AUTHORS: Rohit Raturi, Hayk Sargsyan

  KEYWORDS: Nonlinear Autoregression, Time Series, Prediction, Data Analysis, Deep Learning, Scaled Conjugate Gradient Method, Bayesian Regularization Method

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.9, September 7, 2018

  ABSTRACT: This article is devoted to a time series prediction scheme involving the nonlinear autoregressive algorithm and its applications. The scheme is implemented by means of an artificial neural network containing a hidden layer. As a training algorithm we use scaled conjugate gradient (SCG) method and the Bayesian regularization (BReg) method. The first method is applied to time series without noise, while the second one can also be applied for noisy datasets. We apply the suggested scheme for prediction of time series arising in oil and gas pricing using 50 and 100 past values. Results of numerical simulations are presented and discussed.