In this paper, two feed forward neural network models have been presented to predict the Silicon Modification Level (SiML) of W319 aluminum alloys using the Thermal Analysis (T.A) parameters as inputs. The developed neural networks are a Multilayer Perceptron (MLP) network and a Radial Basis Function (RBF) network. The neural network models were found to predict the SiML accurately (R=0.99). Th...

abstract: this paper represents a novel use of artificial neural networks in medical science. the proposed technique involves training a multi layer perceptron (mlp) (a kind of artificial neural network) with a bp learning algorithm to recognize a pattern for the diagnosing and prediction of five blood disorders, through the results of blood tests from h1 machine. the blood test parameters and ...

In this paper we first show that standard BP algorithm cannot yeild to a uniform information distribution over the neural network architecture. A measure of sensitivity is defined to evaluate fault tolerance of neural network and then we show that the sensitivity of a link is closely related to the amount of information passes through it. Based on this assumption, we prove that the distribu...

Dynamic neural network (DNN) models provide an excellent means for forecasting and prediction of nonstationary time series. A neural network architecture, known as locally recurrent neural network ((LRNN) [71], is preferred to the traditional multilayer perceptron (MLP) because the time varying nature of a stock time series can be better represented using LRNN. The use of LRNN has demonstrated ...

ground vibrations caused by blasting are undesirable results in the mining industry and can cause serious damage to the nearby buildings and facilities; therefore, controlling such vibrations has an important role in reducing the environmental damaging effects. controlling vibration caused by blasting can be achieved once peak particle velocity (ppv) is predicted. in this paper, the values of p...

Abstract A class of recurrent neural networks is developed to solve nonlinear equations, which are approximated by a multilayer perceptron (MLP). The recurrent network includes a linear Hopfield network (LHN) and the MLP as building blocks. This network inverts the original MLP using constrained linear optimization and Newton’s method for nonlinear systems. The solution of a nonlinear equation ...