Neural network is an efficient tool to solve nonlinear problem, but it is hard to determine its structure and it often settles in local minimum. After combining the wavelet with it, the two key problems are both solved. However, a new problem, so called 'dimension disaster', appears, and solving it within the framework of Wavelet Neural Network (WNN) is not easy. We introduce a new neural network named Multiwavelet Neural Network (MWNN), which not only preserves all of the advantages of WNN, but also avoid the 'dimension disaster'. Several theorems are given and the experiment results validate the correctness of our theory.EI
We study artificial neural networks with nonlinear waves as a computing reservoir. We discuss univer...
In this paper, we propose a deep wavelet neural network (DWNN) model to approximate the natural phen...
Target threat assessment is a key issue in the collaborative attack. To improve the accuracy and use...
Neural network is an efficient tool to solve nonlinear problem, but it is hard to determine its stru...
Peti & Krishnaprasad [1] first studied the connection between neural networks and wavelet transforms...
The combination of wavelet theory and neural networks has lead to the development of wavelet network...
This book treats wavelet networks which unify universal approximation features of neuronal networks ...
Neural networks have been effective in several engineering applications because of their learning ab...
This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and...
With technological innovations progressing rapidly, big data is now produced from various applicatio...
Wavelet functions have been used as the activation function in feedforward neural networks. An abund...
paper presents a wavelet neural-network This chaotic time series prediction. Wavelet-for are inspire...
A new class of wavelet networks (WN's) is proposed for nonlinear system identification. In the new n...
Spatio-spectral properties of the Wavelet Transform provide a useful theoretical framework to invest...
A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new ne...
We study artificial neural networks with nonlinear waves as a computing reservoir. We discuss univer...
In this paper, we propose a deep wavelet neural network (DWNN) model to approximate the natural phen...
Target threat assessment is a key issue in the collaborative attack. To improve the accuracy and use...
Neural network is an efficient tool to solve nonlinear problem, but it is hard to determine its stru...
Peti & Krishnaprasad [1] first studied the connection between neural networks and wavelet transforms...
The combination of wavelet theory and neural networks has lead to the development of wavelet network...
This book treats wavelet networks which unify universal approximation features of neuronal networks ...
Neural networks have been effective in several engineering applications because of their learning ab...
This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and...
With technological innovations progressing rapidly, big data is now produced from various applicatio...
Wavelet functions have been used as the activation function in feedforward neural networks. An abund...
paper presents a wavelet neural-network This chaotic time series prediction. Wavelet-for are inspire...
A new class of wavelet networks (WN's) is proposed for nonlinear system identification. In the new n...
Spatio-spectral properties of the Wavelet Transform provide a useful theoretical framework to invest...
A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new ne...
We study artificial neural networks with nonlinear waves as a computing reservoir. We discuss univer...
In this paper, we propose a deep wavelet neural network (DWNN) model to approximate the natural phen...
Target threat assessment is a key issue in the collaborative attack. To improve the accuracy and use...