A fully local algorithm which can automatically detect and learn an unknown pattern is proposed for a mutually connected recurrent neural network, and its fundamental properties are numerically analyzed. the algorithm is applied to chaotic neural networks composed of neuron models with spatiotemporal inputs and refractoriness and to conventional mutually connected neural networks. It is shown that the former could learn more patterns with greater robustness than the latter
Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe view...
Recurrent neural network models with parallel distributed architecture are constructed using ordinar...
The literature on chaos theory reports numerous Neural Networks (NNs) in which the individual neuron...
Many practical applications of neural networks require the identification of strongly non-linear (e....
Training recurrent neural networks (RNNs) is a long-standing open problem both in theoretical neuros...
Many practical applications of neural networks require the identification of nonlinear deterministic...
Abstract—Chaotic neural networks have received a great deal of attention these last years. In this p...
International audienceMany research works deal with chaotic neural networks for various fields of ap...
Summary. Traditional Pattern Recognition (PR) systems work with the model that the object to be reco...
Most known learning algorithms for dynamic neural networks in non-stationary environments need globa...
This paper proposes a new dynamical memory system based on chaotic neural networks, and its learning...
The pioneering contribution of this paper is to design and implement a Neural Network (NN) that demo...
An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single m...
We propose Hebb-like learning rules to store a static pattern as a dynamical attractor in a neural n...
A nonlinear recurrent neural network is trained to synthesize chaotic signals. The identification pr...
Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe view...
Recurrent neural network models with parallel distributed architecture are constructed using ordinar...
The literature on chaos theory reports numerous Neural Networks (NNs) in which the individual neuron...
Many practical applications of neural networks require the identification of strongly non-linear (e....
Training recurrent neural networks (RNNs) is a long-standing open problem both in theoretical neuros...
Many practical applications of neural networks require the identification of nonlinear deterministic...
Abstract—Chaotic neural networks have received a great deal of attention these last years. In this p...
International audienceMany research works deal with chaotic neural networks for various fields of ap...
Summary. Traditional Pattern Recognition (PR) systems work with the model that the object to be reco...
Most known learning algorithms for dynamic neural networks in non-stationary environments need globa...
This paper proposes a new dynamical memory system based on chaotic neural networks, and its learning...
The pioneering contribution of this paper is to design and implement a Neural Network (NN) that demo...
An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single m...
We propose Hebb-like learning rules to store a static pattern as a dynamical attractor in a neural n...
A nonlinear recurrent neural network is trained to synthesize chaotic signals. The identification pr...
Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe view...
Recurrent neural network models with parallel distributed architecture are constructed using ordinar...
The literature on chaos theory reports numerous Neural Networks (NNs) in which the individual neuron...
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