1999
DOI: 10.1201/9781420049176.ch6
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Learning Long-Term Dependencies in NARX Recurrent Neural Networks

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Cited by 105 publications
(143 citation statements)
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“…To deal with long time lags between relevant events, several sequence processing methods were proposed, including Focused BP based on decay factors for activations of units in RNNs (Mozer, 1989(Mozer, , 1992, Time-Delay Neural Networks (TDNNs) (Lang et al, 1990) and their adaptive extension (Bodenhausen and Waibel, 1991), Nonlinear AutoRegressive with eXogenous inputs (NARX) RNNs (Lin et al, 1996), certain hierarchical RNNs (Hihi and Bengio, 1996) (compare Sec. 5.10, 1991), RL economies in RNNs with WTA units and local learning rules (Schmidhuber, 1989b), and other methods (e.g., Ring, 1993Ring, , 1994Plate, 1993;de Vries and Principe, 1991;Sun et al, 1993a;Bengio et al, 1994).…”
Section: Ideas For Dealing With Long Time Lags and Deep Capsmentioning
confidence: 99%
“…To deal with long time lags between relevant events, several sequence processing methods were proposed, including Focused BP based on decay factors for activations of units in RNNs (Mozer, 1989(Mozer, , 1992, Time-Delay Neural Networks (TDNNs) (Lang et al, 1990) and their adaptive extension (Bodenhausen and Waibel, 1991), Nonlinear AutoRegressive with eXogenous inputs (NARX) RNNs (Lin et al, 1996), certain hierarchical RNNs (Hihi and Bengio, 1996) (compare Sec. 5.10, 1991), RL economies in RNNs with WTA units and local learning rules (Schmidhuber, 1989b), and other methods (e.g., Ring, 1993Ring, , 1994Plate, 1993;de Vries and Principe, 1991;Sun et al, 1993a;Bengio et al, 1994).…”
Section: Ideas For Dealing With Long Time Lags and Deep Capsmentioning
confidence: 99%
“…The above scenario comes true for all recurrent structures. However, one can postpone vanishing of gradient in NARX recurrent neural networks with increasing the number of delays in the output delay line of this architecture [6]. As it may be seen in Fig.…”
Section: Narx Recurrent Neural Networkmentioning
confidence: 99%
“…if it is to store information for a long period of time in the presence of noise, then for a term with u << t , 0 ) ( / ) ( ® ¶ ¶ u y t y [5]. In this condition, the gradient decays exponentially [6], meaning that there is not any chance for the terms that are far from t to change the weights in such a way that allow the network's state to jump to a better basin of attraction. The above scenario comes true for all recurrent structures.…”
Section: Narx Recurrent Neural Networkmentioning
confidence: 99%
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