Add to ORKGA class of data-reusing learning algorithms for real-time recurrent neural networks (RNNs) is analyzed. The analysis is undertaken for a general sigmoid nonlinear activation function of a neuron for the real time recur-rent learning training algorithm. Error bounds and convergence condi-tions for such data-reusing algorithms are provided for both contractive and expansive activation functions. The analysis is undertaken for var-ious congurations that are generalizations of a linear structure innite impulse response adaptive lter.
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
In this work the technique o f creation o f adapthre training algorithms for recurrent neural networ...
We provide the relationship between the learning rate and the slope of a nonlinear activation functi...
A class of data-reusing learning algorithms for real-time recurrent neural networks (RNNs) is analyz...
A real time recurrent learning (RTRL) algorithm with an adaptive-learning rate for nonlinear adaptiv...
An adaptive amplitude real time recurrent learning (AARTRL) algorithm for fully connected recurrent ...
A complex-valued real-time recurrent learning (CRTRL) algorithm for the class of nonlinear adaptive ...
A relationship between the learning rate · in the learning algorithm, and the slope fl in the nonlin...
A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonline...
“Recurrent neural networks (RNN) attract considerable interest in computational intelligence because...
A new approach for the adaptive algorithm of a fully connected recurrent neural network (RNN) based ...
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods ...
The authors provide relationships between the a priori and a posteriori errors of adaptation algorit...
This paper reviews different approaches to improving the real time recurrent learning (RTRL) algorit...
Recurrent neural networks (RNNs) offer flexible machine learning tools which share the learning abil...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
In this work the technique o f creation o f adapthre training algorithms for recurrent neural networ...
We provide the relationship between the learning rate and the slope of a nonlinear activation functi...
A class of data-reusing learning algorithms for real-time recurrent neural networks (RNNs) is analyz...
A real time recurrent learning (RTRL) algorithm with an adaptive-learning rate for nonlinear adaptiv...
An adaptive amplitude real time recurrent learning (AARTRL) algorithm for fully connected recurrent ...
A complex-valued real-time recurrent learning (CRTRL) algorithm for the class of nonlinear adaptive ...
A relationship between the learning rate · in the learning algorithm, and the slope fl in the nonlin...
A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonline...
“Recurrent neural networks (RNN) attract considerable interest in computational intelligence because...
A new approach for the adaptive algorithm of a fully connected recurrent neural network (RNN) based ...
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods ...
The authors provide relationships between the a priori and a posteriori errors of adaptation algorit...
This paper reviews different approaches to improving the real time recurrent learning (RTRL) algorit...
Recurrent neural networks (RNNs) offer flexible machine learning tools which share the learning abil...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
In this work the technique o f creation o f adapthre training algorithms for recurrent neural networ...
We provide the relationship between the learning rate and the slope of a nonlinear activation functi...