Add to ORKGA relationship between the learning rate · in the learning algorithm, and the slope fl in the nonlinear activation function, for a class of recurrent neural networks (RNNs) trained by the real-time recurrent learning algo-rithm is provided. It is shown that an arbitrary RNN can be obtained via the referent RNN, with some deterministic rules imposed on its weights and the learning rate. Such relationships reduce the number of degrees of freedom when solving the nonlinear optimization task of finding the optimal RNN parameters.
A complex-valued real-time recurrent learning (CRTRL) algorithm for the class of nonlinear adaptive ...
Nonlinear system identification and prediction is a complex task, and often non-parametric models su...
“Recurrent neural networks (RNN) attract considerable interest in computational intelligence because...
A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonline...
We provide the relationship between the learning rate and the slope of a nonlinear activation functi...
A real time recurrent learning (RTRL) algorithm with an adaptive-learning rate for nonlinear adaptiv...
A class of data-reusing learning algorithms for real-time recurrent neural networks (RNNs) is analyz...
An adaptive amplitude real time recurrent learning (AARTRL) algorithm for fully connected recurrent ...
Abstract. Recurrent neural networks are often used for learning time-series data. Based on a few ass...
We address the choice of the coefficients in the cost function of a modular nested recurrent neural-...
This paper reviews different approaches to improving the real time recurrent learning (RTRL) algorit...
Technical ReportThis paper describes a special type of dynamic neural network called the Recursive N...
Abstract—We address the choice of the coefficients in the cost function of a modular nested recurren...
Automatic nonlinear-system identification is very useful for various disciplines including, e.g., au...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
A complex-valued real-time recurrent learning (CRTRL) algorithm for the class of nonlinear adaptive ...
Nonlinear system identification and prediction is a complex task, and often non-parametric models su...
“Recurrent neural networks (RNN) attract considerable interest in computational intelligence because...
A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonline...
We provide the relationship between the learning rate and the slope of a nonlinear activation functi...
A real time recurrent learning (RTRL) algorithm with an adaptive-learning rate for nonlinear adaptiv...
A class of data-reusing learning algorithms for real-time recurrent neural networks (RNNs) is analyz...
An adaptive amplitude real time recurrent learning (AARTRL) algorithm for fully connected recurrent ...
Abstract. Recurrent neural networks are often used for learning time-series data. Based on a few ass...
We address the choice of the coefficients in the cost function of a modular nested recurrent neural-...
This paper reviews different approaches to improving the real time recurrent learning (RTRL) algorit...
Technical ReportThis paper describes a special type of dynamic neural network called the Recursive N...
Abstract—We address the choice of the coefficients in the cost function of a modular nested recurren...
Automatic nonlinear-system identification is very useful for various disciplines including, e.g., au...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
A complex-valued real-time recurrent learning (CRTRL) algorithm for the class of nonlinear adaptive ...
Nonlinear system identification and prediction is a complex task, and often non-parametric models su...
“Recurrent neural networks (RNN) attract considerable interest in computational intelligence because...