In this paper I describe an evolutionary wavelet network to optimize the filtering of a statisticaltime series into separate contributions. The wavelet base is regarded as a neural network where thenetwork nodes are discrete wavelet transforms, the wavelon, and the network structure and parameters areselected through evolutionary techniques. With this combined approach I can separate stochastic fromstructural components within an optimized framework and finally I can perform optimized predictiveanalysis on the time series components
paper presents a wavelet neural-network This chaotic time series prediction. Wavelet-for are inspire...
Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used ...
In this dissertation, the effectiveness of Wavelet Packet Multi-Layer Perceptrons (WP-MLP) neural n...
In this paper I describe an evolutionary wavelet network to optimize the filtering of a statisticalt...
In this paper I describe an evolutionary wavelet network to optimize the filtering of a statistical ...
This article reviews the role of wavelets in statistical time series analysis. We survey work that e...
International audienceThis paper deals with methods for finding the suitable weights in an Artificia...
This article reviews the role of wavelets in statistical time series analysis. We survey work that e...
This article defines and studies a new class of non-stationary random processes constructed from dis...
This article defines and studies a new class of non-stationary random processes constructed from dis...
My thesis investigates wavelet theory and methods underlying recent applications to time series anal...
In this paper I describe a wavelet filtering approach to separate a time series, the signal, into it...
[[abstract]]Time series are an important and interesting research field due to their many different ...
In this paper we investigate the effective design of an appropriate neural network model for time se...
This chapter presents a hybrid Evolutionary Computation/Neural Network combination for time series p...
paper presents a wavelet neural-network This chaotic time series prediction. Wavelet-for are inspire...
Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used ...
In this dissertation, the effectiveness of Wavelet Packet Multi-Layer Perceptrons (WP-MLP) neural n...
In this paper I describe an evolutionary wavelet network to optimize the filtering of a statisticalt...
In this paper I describe an evolutionary wavelet network to optimize the filtering of a statistical ...
This article reviews the role of wavelets in statistical time series analysis. We survey work that e...
International audienceThis paper deals with methods for finding the suitable weights in an Artificia...
This article reviews the role of wavelets in statistical time series analysis. We survey work that e...
This article defines and studies a new class of non-stationary random processes constructed from dis...
This article defines and studies a new class of non-stationary random processes constructed from dis...
My thesis investigates wavelet theory and methods underlying recent applications to time series anal...
In this paper I describe a wavelet filtering approach to separate a time series, the signal, into it...
[[abstract]]Time series are an important and interesting research field due to their many different ...
In this paper we investigate the effective design of an appropriate neural network model for time se...
This chapter presents a hybrid Evolutionary Computation/Neural Network combination for time series p...
paper presents a wavelet neural-network This chaotic time series prediction. Wavelet-for are inspire...
Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used ...
In this dissertation, the effectiveness of Wavelet Packet Multi-Layer Perceptrons (WP-MLP) neural n...