Sentence examples for fully recurrent from inspiring English sources

The RNN as Complex Time Delay Fully Recurrent Neural Network (CTDFRNN) block can be combined at the output using time averaging and Self Organization Map (SOM -based optimization, yielding a new architectural framework.

Dynamical reservoirs are generally formed by a fully recurrent neural network with fixed, typically sparse, random weights, and one further layer of external units connected to the internal units to read out the dynamics of the network.

The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability.

A recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics.

The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines.

Patients are prescribed complex antiplatelet therapies, which do not fully prevent recurrent thromboembolic events.

The GLR PNN is derived from the original PNN by incorporating a fully connected recurrent layer between the pattern and output layers.

These solutions can be generated in real time for simple models on modern computer hardware, resulting in a fully interactive recurrent modeling system for designing models that closely match specific natural shapes.

Different from the traditional fully connected recurrent neural networks, an echo state network (ESN) adopts the sparsely connected hidden layer in which the connectivity and weights of hidden neuron nodes are fixed and randomly assigned.

Different from traditional fully connected recurrent neural networks, ESN adopts the sparsely connected hidden layer in which the connectivity and weights of hidden neuron nodes are randomly assigned and unchanged during training.

We use the decision making model introduced by Wang [6], based on a fully connected recurrent network of integrate-and-fire neurons and synaptic currents mediated by AMPA, NMDA, and GABA receptors [16] (see Materials and Methods for details).