Sentence examples for basic recurrent neural network from inspiring English sources
Fig. 1 Basic recurrent neural network language model.
The proposed model is a modified version of the basic recurrent neural network language model [6].
It is observed that the computational complexity is much lower than that in the basic recurrent neural network model.
Basic recurrent neural network language model consists of three layers: input layer, hidden layer and output layer; input word is presented to the network input layer using a 1-of-n encoding.
The proposed model is tested with the English AMI speech recognition dataset and outperforms the baseline n-gram model, the basic recurrent neural network language models (RNNLM) and the GPU-based recurrent neural network language models (CUED-RNNLM) in perplexity and word error rate.
By using basic electronic components, they constructed a recurrent neural network and derived the characteristic equations for the network.
With the experimental data obtained from a set of purposely designed molds with basic feature geometry, a soft-sensor based on a recurrent neural network has been developed to predict the melt-flow-length.
The main contribution of this work is building a recurrent neural network language modeling model that outperforms the basic RNNLM [6].
They also add word features to the recurrent neural network input layer to model the results better than the basic model.
Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations.
