This paper presents a new algorithm for constructing and training wavelet neural network. This algorithm is based on the variation of the number of hidden neurons dynamically during the training process. The suggested method determines the optimal number of the hidden neurons and solves the optimization problem of wavelet neural network structure. The problem of finding a good neural model is t...

Ahsrr-ucr-This paper describes the application of an artificial neural network (ANN) technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals. Three classes of EEG signals were used: Normal, Schizophrenia (SCH), and Obsessive Compulsive Disorder (OCD). The architecture of the artificial neural network used in the classification i...

In this paper, we propose a method of classification of image by combining wavelet transform and neural network. Our main objective in this work is to achieve an optimal approach of classification by combining wavelet transform and neural network. The proposed scheme for successful classification is combination of a wavelet domain feature extractor and back propagation neural networks (BPNN) cl...

In this study, a Mexican Hat Wavelet scalogram neural genetic network approach is proposed for signal classification. The Wavelet Scalogram network uses a Levenberg-Marquardt multilayer feedforward neural network-genetic algorithm hybrid structure, and its input layer constitutes the feature extraction part, whereas the hidden layer and output layer constitute the signal classification part. Fr...

Images have large data quantity, for storage and transmission of images, high efficiency image compression methods are under wide attention. In this Paper we proposed and implemented a wavelet transform and neural network model for image compression which combines the advantage of wavelet transform and neural network. Images are decomposed using Haar wavelet filters into a set of sub bands with...

In order to analyze the evolvement trend of the network threat and to explore the self-perception and control problem of the security situation, the dynamic wavelet neural network model is integrated into the model design, and a kind of network security situation awareness based on the optimized dynamic wavelet neural network is put forward, so as to enhance the interaction and cognitive abilit...

In this paper, the idea of fertilizing fuzzy neural networks with wavelets is borrowed and enhanced to introduce a novel neural network named Auto Regressive eXogenous Local Model (ARXLM) network. The enhanced network has a set of notable features compared with previous published fertilized networks. These features can be summarized as follows. First, the proposed network has a simple and plast...

In the fault diagnosis of the motor, the vibration signals can fully reflect the status of the motor. In this paper, on the basis of wavelet packet fault feature extraction, a new approach for motor fault diagnosis based on wavelet packet analysis and fuzzy RBF neural network was presented.The method gains the energy of characteristic channel of bearing failure vibration signals of asynchronous...

This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn R from scattered samples (xi; y = f(xi)) i=1....n, where first, w...