estimating the final price of products is of great importance. for manufacturing companies proposing a final price is only possible after the design process over. these companies propose an approximate initial price of the required products to the customers for which some of time and money is required. here using the existing data of already designed transformers and utilizing the bayesian anal...

conclusions as we can see the ann outputs values are very close to actual cu concentration, so indicating that predicted values are accurate and the network design is proper and the input variables well suitable for the prediction of cu concentration. background access to safe drinking water is one of the basic human rights and essential for healthy life. concerns about the effects of copper on...

In this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. Experimental data was divided into two parts (70% for training and 30% for testing). Optimal configuration of the network was obtained with minimization of prediction error on testing data. The accuracy of our proposed model was compared with four well-known empirical equations. The arti...

In this paper, an Artificial Neural Networks (ANN) model has been developed to design the multilayer Rectangular microstrip patch. In the design procedure, synthesis ANN model is used as feed forward network to calculate the resonant frequency. Analysis ANN model is used as the reverse side of the problem to calculate the antenna dimension. The network is trained with the data obtained from mea...

In this paper, a Rough-Neuro hybrid methodology of the diagnostic process is proposed as a means to achieve accurate diagnosing of Erythemato-Squamous diseases. The methodology incorporates a two-stage hybrid mechanism. Rough sets Johnson Reducer for reduction of relevant attributes and artificial neural network Levenberg-Marquardt algorithm for the classification of the diseases. The model ach...

This paper describes artificial neural network (ANN) based prediction of theresponse of a fiber optic sensor using evanescent field absorption (EFA). The sensingprobe of the sensor is made up a bundle of five PCS fibers to maximize the interaction ofevanescent field with the absorbing medium. Different backpropagation algorithms areused to train the multilayer perceptron ANN. The Levenberg-Marq...

natural fire inflicting irreparable damage to rangelands and forest areas is cause changes in landscape ecology. the purpose of this research is comparison of artificial neural network (ann) and line regression (lr) models to predict of forest and rangelands fires to this end, the data consist fire burned area and fire were used weather data over a period of 7 years (2006-2012(.the result indic...

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

In this paper a modification on Levenberg-Marquardt algorithm for MLP neural network learning is proposed. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. An example is given to show usefulness of this method. Finally a simulation verifies the results of proposed method. Keywords—Levenberg-Marquardt, modification, neural network,...