Prediction in electronics based on limited information
EHAC'09 Proceedings of the 8th WSEAS international conference on Electronics, hardware, wireless and optical communication
Forecasting based on short time series using ANNs and grey theory - some basic comparisons
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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This paper presents a novel type of higher-order pipelined recurrent neural networks called the second-order pipelined recurrent neural network. The aim of the network is to improve the performance of the pipelined recurrent neural network by accommodating second order terms in the inputs. The network is tested for the prediction of non-linear and non-stationary signals. Two physical time-series, which are the mean value of the AE index and the sunspot signals are used in the simulation. The simulation results showed an average improvement in the signal to noise ratio of 6.09 dB when compared to the pipelined recurrent neural networks.