In the paper new non-conventional growing neural network is proposed. It coincides with the Cascade- Correlation Learning Architecture structurally, but uses ortho-neurons as basic structure units, which can be adjusted using linear tuning procedures. As compared with conventional approximating neural networks proposed approach allows significantly to reduce time required for weight coefficients adjustment and the training dataset size
AbstractArtificial neural network is a computational algorithm that mimics the workings of nerve cel...
The long course of evolution has given the human brain many desirable characteristics not present in...
Feed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons ar...
In the paper, an ontogenic artificial neural network (ANNs) is proposed. The network uses orthogonal...
this paper is to discuss neural networks, which are based on orthogonal expansions (OE-nets) of unkn...
In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural N...
Both the input and link weights of process neural network can be all time-various functions, an aggr...
This paper presents some ideas about a new neural network architecture that can be compared to a Tay...
With the growing emphasis on autonomy, intelligence and an increased amount of information required ...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
Neural networks play an important role in the execution of goal-oriented paradigms. They offer flex...
[[abstract]]This paper presents a novel dynamic structural neural network (DSNN) and a learning algo...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
We provide the relationship between the learning rate and the slope of a nonlinear activation functi...
Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the so-called Multi-Layer Percept...
AbstractArtificial neural network is a computational algorithm that mimics the workings of nerve cel...
The long course of evolution has given the human brain many desirable characteristics not present in...
Feed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons ar...
In the paper, an ontogenic artificial neural network (ANNs) is proposed. The network uses orthogonal...
this paper is to discuss neural networks, which are based on orthogonal expansions (OE-nets) of unkn...
In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural N...
Both the input and link weights of process neural network can be all time-various functions, an aggr...
This paper presents some ideas about a new neural network architecture that can be compared to a Tay...
With the growing emphasis on autonomy, intelligence and an increased amount of information required ...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
Neural networks play an important role in the execution of goal-oriented paradigms. They offer flex...
[[abstract]]This paper presents a novel dynamic structural neural network (DSNN) and a learning algo...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
We provide the relationship between the learning rate and the slope of a nonlinear activation functi...
Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the so-called Multi-Layer Percept...
AbstractArtificial neural network is a computational algorithm that mimics the workings of nerve cel...
The long course of evolution has given the human brain many desirable characteristics not present in...
Feed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons ar...