A new type of Wavelet Neural Network (WNN) has been proposed to enhance the function approximation and generalization performance generating an optimal size network. In the proposed WNN, the nonlinear activation function is a linear combination of wavelets that can be updated during the network training process. As a result the approximate error is significantly decreased. The RBF network based...

This work compares a few attempts based on Wavelet and Neural networks, for extracting the Jominy hardness pro les of steels directly from the chemical composition. That is essentially a black-box modeling problem: Wavelet and Neural networks seem powerful, especially when compared with classical methods commonly found in literature. In particular, the paper proposes a multi-network architectur...

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series are introduced. In addition, multi neural network for load forecast including BP artificial neural network, RBF neural network and wavelet neural network are introduced, too. Then, a combination load forecasting model for power load based on chaotic time series,...

The main goal of the present work is to decrease the computational burden for optimum design of steel frames with frequency constraints using a new type of neural networks called Wavelet Neural Network. It is contested to train a suitable neural network for frequency approximation work as the analysis program. The combination of wavelet theory and Neural Networks (NN) has lead to the developmen...

In this work we propose a method for learning wavelet filters directly from data. We accomplish this by framing the discrete wavelet transform as a modified convolutional neural network. We introduce an autoencoder wavelet transform network that is trained using gradient descent. We show that the model is capable of learning structured wavelet filters from synthetic and real data. The learned w...

The wavelet network 22, 23] has been introduced as a special feedforward neural network supported by the wavelet theory. Such network can be directly used in function approximation problems, and consequently can be applied to nonlinear system modeling by means of nonlinear black-box identiication. In this paper the construction of feedforward neural networks is discussed from both identiication...

Application of artificial neural network (ANN) in forward kinematic solution (FKS) of a novel co-axial parallel mechanism with six degrees of freedom (6-DOF) is addressed in Current work. The mechanism is known as six revolute-spherical-universal (RSU) and constructed by 6-RSU co-axial kinematic chains in parallel form. First, applying geometrical analysis and vectorial principles the kinematic...

In this paper, we present a new predictive hybrid model using discrete wavelet transform (DWT), and the artificial neural network (ANN) to reduce the bullwhip effect of demand in supply chain to obtain a real amount of final customer demand. Also, we compare our result with more comprehensive sample of previous research to extend the scope of our study. In this new research our methodology is c...