Add to ORKGA major problem in applying neural networks is the determination of the size of the network. Even for moderate networks the number of parameters can become high with respect to the number of data used in learning. In this paper we examine network performance while reducing the size of the network. The reduction is based on graphical analysis of network output per hidden layer cell and input layer cell. Performance is measured as the sum of squared residuals as well as by the value of largest Lyapunov exponents which is a measure of dynamic instability of time series.Neurale netwerken, Tijdreeksen
The paper presents the results of building neural network predictive models of the occupancy of the ...
The field of neural networks is a wide and diverse field which spans a variety of interests, modelli...
Applicability of neural nets in time series forecasting has been considered and researched. For this...
A major problem in applying neural networks is specifying the size of the network. Even for moderate...
textabstractThe flexibility of neural networks to handle complex data patterns of economic variables...
In this work, I will describe a new approach for time series non linearity testing by means of neura...
The problem of predicting nonlinear and nonstationary signals is complex since the physical law that...
This research work investigates the possibility to apply several neural network architectures for si...
The analysis of a time series is a problem well known to statisticians. Neural networks form the bas...
Neural Network approaches to time series prediction are briefly discussed, and the need to find the ...
In this paper neural networks are fitted to the real exchange rates of seven industrialized countrie...
Designing neural network (NN) to predict time series is not a trivial task. Some kind of science and...
The dynamics of data traffic intensity is examined using traffic measurements at the interface switc...
Nowadays neural networks (NN) are applied in the most various fields and are actually receiving a lo...
Abstract: The following paper tries to develop a simple neural network approach to analyse time seri...
The paper presents the results of building neural network predictive models of the occupancy of the ...
The field of neural networks is a wide and diverse field which spans a variety of interests, modelli...
Applicability of neural nets in time series forecasting has been considered and researched. For this...
A major problem in applying neural networks is specifying the size of the network. Even for moderate...
textabstractThe flexibility of neural networks to handle complex data patterns of economic variables...
In this work, I will describe a new approach for time series non linearity testing by means of neura...
The problem of predicting nonlinear and nonstationary signals is complex since the physical law that...
This research work investigates the possibility to apply several neural network architectures for si...
The analysis of a time series is a problem well known to statisticians. Neural networks form the bas...
Neural Network approaches to time series prediction are briefly discussed, and the need to find the ...
In this paper neural networks are fitted to the real exchange rates of seven industrialized countrie...
Designing neural network (NN) to predict time series is not a trivial task. Some kind of science and...
The dynamics of data traffic intensity is examined using traffic measurements at the interface switc...
Nowadays neural networks (NN) are applied in the most various fields and are actually receiving a lo...
Abstract: The following paper tries to develop a simple neural network approach to analyse time seri...
The paper presents the results of building neural network predictive models of the occupancy of the ...
The field of neural networks is a wide and diverse field which spans a variety of interests, modelli...
Applicability of neural nets in time series forecasting has been considered and researched. For this...