Hidden Markov Models (HMMs) are much simpler than Recurrent Neural Networks (RNNs), and rely on strong assumptions which may not always be true. If the assumptions are true then you may see better performance from an HMM since it is less finicky to get working.

2015年11月3日 · For purposes of discussion I'll assume you are using RNN for the typical use case of time series analysis, where the recurrence operation allows response to depend on a time-evolving state; for example the network can now detect changes over time. This is exactly the added capability you'd want a Recurrent Neural Network for, in this example.

2015年5月23日 · Recurrent Neural Networks (RNN) basically unfolds over time. It is used for sequential inputs where the time factor is the main differentiating factor between the elements of the sequence. For example, here is a recurrent neural network used for language modeling that has been unfolded over time.

2019年9月17日 · Recurrent Neural Networks are more general than Kalman filter. One could actually train a RNN to simulate a Kalman filter. Neural nets are kind of black box models and weights and activations are very often not interpretable (above all in the deeper layers).

Recurrent neural nets are neural networks that keep state between input samples. They remember previous input samples and use those to help classify the current input sample. They are mostly useful when the order of your data is important.

RNN is a time based neural network.. at the end of time steps ( length of the input) it forms a vector which represents a thought preserving sequence information across the time. Thinking of thought vector like some sort of figure or object might help, which gets it's proper shape (depending on the input sequence) through time steps depending ...

2016年10月10日 · Given it's a recurrent neural network it could be related to the timing of evaluation of neurons in the network (i.e. the feedback of the hidden neurons to themselves) but it isn't clear at all. Also I assume the $-A_j$ term in (1) is effectively a connection of the neuron back to itself with a $-1$ weight?

2015年3月6日 · Why do recurrent neural networks (RNNs) have a tendency to suffer from vanishing/exploding gradient? For what a vanishing/exploding gradient is, see Pascanu, et al. (2013). On the difficulty of tr...

There is the RSNNS package that provides access to the "Stuttgart Neural Network Simulator" (SNNS). It contains the classical recurrent network structures of types 'Jordan' and 'Elman'. SNNS is a bit old (before 2000), but may still be worth a try. The R package itself has been updated in September this year.

Zimmermann, Grothmann, Tietz, von Jouanne-Diedrich: Market Modeling, Forecasting and Risk Analysis with Historical Consistent Neural Networks. Just to give an idea about the paradigm here is a short excerpt: In this article, we present a new type of recurrent NN, called historical consistent neural network (HCNN).