Happening of Ferroresonance phenomenon is one of the reasons of consuming and ruining transformers, so recognition of Ferroresonance phenomenon has a special importance. A novel method for classification of Ferroresonance presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet...

We introduce a scattering representation for the analysis and classification of sounds. It is locally translation-invariant, stable to deformations in time and frequency, and has the ability to capture harmonic structures. The scattering representation can be interpreted as a convolutional neural network which cascades a wavelet transform in time and along a harmonic spiral. We study its applic...

In this Present study, the technique of wavelet transform and neural network were developed for speech based text-dependent and text0independent speaker identification. 390 feature were fed to feed-forward back propagation neural network for classification The function of feature extraction and classification are performed using wavelet and neural network system. The declared result shows that ...

In this paper we suggest a novel method which is robust and efficient in extracting eye windows using Wavelets and Neural Networks. Wavelet analysis is used as a pre-processor for a back propagation neural network with conjugate gradient learning. The inputs to the neural network are the wavelet maxima neighborhood coefficients of face images at a particular scale. The output of the neural netw...

In this paper, the use of wavelet analysis and artificial neural networks for generating artificial earthquake accelerograms from target spectrum is suggested as a new method. This procedure uses the learning capabilities of neural network to expand the knowledge of the inverse mapping from response spectrum to earthquake accelerogram. In the first step, wavelet analysis is used to decompose ea...

We discuss approximation of functions using deep neural nets. Given a function f on a d-dimensional manifold Γ ⊂ R, we construct a sparsely-connected depth-4 neural network and bound its error in approximating f . The size of the network depends on dimension and curvature of the manifold Γ, the complexity of f , in terms of its wavelet description, and only weakly on the ambient dimension m. Es...

Explosive ordnance disposal robot path planning is a nondeterministic polynomial time (NP) problem, traditional optimization methods are not very effective to it, which are easy to plunge into local minimum. In this paper, a new algorithm-Danger Model Immune Wavelet Neural Network (DIWNN) is improved. It is proposed to initialize the weights and biases of wavelet neural network (WNN), the ergod...