Computational capabilities of recurrent NARX neural networks
نویسندگان
چکیده
منابع مشابه
Computational Capabilities of Recurrent NARX Neural Networks - Systems, Man and Cybernetics, Part B, IEEE Transactions on
Recently, fully connected recurrent neural networks have been proven to be computationally rich—at least as powerful as Turing machines. This work focuses on another network which is popular in control applications and has been found to be very effective at learning a variety of problems. These networks are based upon Nonlinear AutoRegressive models with eXogenous Inputs (NARX models), and are ...
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It has previously been shown that gradient-descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long-term dependencies, i.e. those problems for which the desired output depends on inputs presented at times far in the past. We show that the long-term dependencies problem is lessened for a class of architectures called nonlinear autoregressive models ...
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Recurrent neural networks (RNNs) have shown success for many sequence-modeling tasks, but learning long-term dependencies from data remains difficult. This is often attributed to the vanishing gradient problem, which shows that gradient components relating a loss at time t to time t− τ tend to decay exponentially with τ . Long short-term memory (LSTM) and gated recurrent units (GRUs), the most ...
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It has recently been shown that gradient-descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long-term dependencies, i.e., those problems for which the desired output depends on inputs presented at times far in the past. We show that the long-term dependencies problem is lessened for a class of architectures called Nonlinear AutoRegressive models w...
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Recurrent neural networks are important models of neural computation This work relates the power of them to those of other conventional models of computation like Turing machines and nite automata and proves results about their learning capabilities Speci cally it shows a Probabilistic recurrent networks and Probabilistic turing ma chine models are equivalent b Probabilistic recurrent networks ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
سال: 1997
ISSN: 1083-4419,1941-0492
DOI: 10.1109/3477.558801