Add to ORKGWe propose a Spatial Artificial Neural Network (SANN) with spatial architecture which consists of a multilayer feedforward neural network with hidden units adopt recurrent lateral inhibition connection, all input and hidden neurons have synapses connections with the output neurons. In addition, a supervised learning algorithm based on error back propagation is developed. The proposed network has shown a superior generalization capability in simulations with pattern recognition and non-linear function approximation problems. And, the experimental also shown that SANN has the capability of avoiding local minima problem
<div><p>In this study, a novel spatial filter design method is introduced. Spatial filtering is an i...
tput) is very commonly used to approximate unknown mappings. If the output layer is linear, such a n...
It is a neural network truth universally acknowledged, that the signal transmitted to a target node ...
We propose a Spatial Artificial Neural Network (SANN) with spatial architecture which consists of a ...
A spatially connected artificial neural network (ANN) is proposed. The ANN parameters are tuned usin...
Artificial neural network models, particularly the perceptron and the backpropagation network, do no...
It has been shown that a nonmonotone neural network model can recognize spatiotemporal patterns with...
A neural network model called lateral interaction in accumulative computation for detection of non-r...
The hidden layer neurons in a multi-layered feed-forward neural network serve a critical role. From ...
A neural network model called lateral interaction in accumulative computation for detection of non-r...
Abstract — Cell assembly is one of explanations of information processing in the brain, in which an ...
Starting from the properties of a neural network with backward lateral inhibitions, we define a new ...
A new neural network architecture for spatial patttern recognition using multi-scale pyramida1 codin...
Local competition among neighboring neurons is common in biological neu-ral networks (NNs). In this ...
The distributed outstar, a generalization of the outstar neural network for spatial pattern learning...
<div><p>In this study, a novel spatial filter design method is introduced. Spatial filtering is an i...
tput) is very commonly used to approximate unknown mappings. If the output layer is linear, such a n...
It is a neural network truth universally acknowledged, that the signal transmitted to a target node ...
We propose a Spatial Artificial Neural Network (SANN) with spatial architecture which consists of a ...
A spatially connected artificial neural network (ANN) is proposed. The ANN parameters are tuned usin...
Artificial neural network models, particularly the perceptron and the backpropagation network, do no...
It has been shown that a nonmonotone neural network model can recognize spatiotemporal patterns with...
A neural network model called lateral interaction in accumulative computation for detection of non-r...
The hidden layer neurons in a multi-layered feed-forward neural network serve a critical role. From ...
A neural network model called lateral interaction in accumulative computation for detection of non-r...
Abstract — Cell assembly is one of explanations of information processing in the brain, in which an ...
Starting from the properties of a neural network with backward lateral inhibitions, we define a new ...
A new neural network architecture for spatial patttern recognition using multi-scale pyramida1 codin...
Local competition among neighboring neurons is common in biological neu-ral networks (NNs). In this ...
The distributed outstar, a generalization of the outstar neural network for spatial pattern learning...
<div><p>In this study, a novel spatial filter design method is introduced. Spatial filtering is an i...
tput) is very commonly used to approximate unknown mappings. If the output layer is linear, such a n...
It is a neural network truth universally acknowledged, that the signal transmitted to a target node ...