We discuss the adaptable Boolean net neural paradigm together with its learning and generalization properties. Usually, neural networks are considered as classifiers and very few attempts have been made to explore their different computational capabilities. Here, we discuss a different paradigm for the, adaptable, Boolean neural networks
To train a Boolean neural network to comment on the progress of the task it is controlling, we have ...
In this paper we investigate multi-layer perceptron networks in the task domain of Boolean functions...
We present alternative algorithms that avoid the combinatorial explosion problem, and that emerge ro...
We discuss the adaptable Boolean net neural paradigm together with its learning and generalization p...
In sequence learning studies we can distinguish two fundamental approaches: general-regularity lea...
A multi-layered neural assembly is developed which has the capability of learning arbitrary Boolean ...
In this paper a Boolean neural networks which is able to learn and to control temporal sequences of ...
This report surveys some connections between Boolean functions and artificial neural networks. The f...
This report surveys some connections between Boolean functions and artificial neural networks. The f...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
Here, we discuss a Boolean neural networks which is able to learn and to control temporal sequences ...
Sequence learning has a variety of different approaches. We can distinguish two fundamental approach...
The most commonly used neural network models are not well suited to direct digital implementations b...
Boolean networks (BNs) have been mainly considered as genetic regulatory network models and are the ...
To train a Boolean neural network to comment on the progress of the task it is controlling, we have ...
In this paper we investigate multi-layer perceptron networks in the task domain of Boolean functions...
We present alternative algorithms that avoid the combinatorial explosion problem, and that emerge ro...
We discuss the adaptable Boolean net neural paradigm together with its learning and generalization p...
In sequence learning studies we can distinguish two fundamental approaches: general-regularity lea...
A multi-layered neural assembly is developed which has the capability of learning arbitrary Boolean ...
In this paper a Boolean neural networks which is able to learn and to control temporal sequences of ...
This report surveys some connections between Boolean functions and artificial neural networks. The f...
This report surveys some connections between Boolean functions and artificial neural networks. The f...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
Here, we discuss a Boolean neural networks which is able to learn and to control temporal sequences ...
Sequence learning has a variety of different approaches. We can distinguish two fundamental approach...
The most commonly used neural network models are not well suited to direct digital implementations b...
Boolean networks (BNs) have been mainly considered as genetic regulatory network models and are the ...
To train a Boolean neural network to comment on the progress of the task it is controlling, we have ...
In this paper we investigate multi-layer perceptron networks in the task domain of Boolean functions...
We present alternative algorithms that avoid the combinatorial explosion problem, and that emerge ro...
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