In recent years, great attention has been paid to the development of high manganese austenitic TWIP steels exhibiting high tensile strength and exceptional total elongation. Due to low stacking fault energy (SFE), cross slip becomes more difficult in these steels and mechanical twinning is then the favored deformation mode besides dislocation gliding. Chemical composition along with processing ...

Several artificial neural network (ANN) models with a feed-forward, back-propagation network structure and various training algorithms, are developed to forecast daily and monthly river flow discharges in Manwan Reservoir. In order to test the applicability of these models, they are compared with a conventional time series flow prediction model. Results indicate that the ANN models provide bett...

The use of artificial neural networks (ANN) for nonlinear system modeling is a field where still there is much theoretical work to be done. A structured ANN which obtains neural models of nonlinear systems is presented. Those neural models are Fourier-series based. To check the goodness of the method, conventional difference equations are re-modeled via ANN and their respective input/outputs co...

The main focus of the PhD thesis is about automating the implementation of artificial neural networks (ANNS) models by applying object/ and component technology. Though various ANN models exist, the aspect of how to provide reusable components in that domain for efficiently implementing adequate system architectures has been barely investigated. The prototypical component framework that was des...

Problem: Are there, for practical uses, any benefits of Artificial Neural Network analyses (ANN) compared with logistic regression analyses? Data: A random sample of 2,294 70-year-old persons from Göteborg, Sweden, was investigated through interviews and medical examinations. Methods: Seven-year mortality was studied by neural network analysis using the SPSS module Clementine 9.0 and by standar...

ABSTRACT Genetic algorithm (multiparameter linear regression; GA-MLR) and genetic algorithm-artificial neural network (GA-ANN) global models have been used for prediction of the toxicity of phenols to Tetrahymena pyriformis. The data set was divided into 150 molecules for training, 50 molecules for validation, and 50 molecules for prediction sets. A large number of descriptors were calculated a...

The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. While ANN by its non-linear nature is effective to predict cloudy days, A...

In this study, artificial neural network (ANN) and multiple regression (MR) models were developed to predict the critical factor of safety (Fs) of the homogeneous finite slopes subjected to earthquake forces. To achieve this, the values of Fs in 5184 nos. of homogeneous finite slopes having different slope, soil and earthquake parameters were calculated by using the Simplified Bishop method and...