In this paper, load-carrying capacity in steel shear wall (SSW) was estimated using artificial neural networks (ANNs). The SSW parameters including load-carrying capacity (as ANN’s target), plate thickness, thickness of stiffener, diagonal stiffener distance, horizontal stiffener distance and gravity load (as ANN’s inputs) are used in this paper to train the ANNs. 144 samples data of each of th...

The purpose of the work described in this paper is to investigate the use of autoregressive (AR) model by using maximum likelihood estimation (MLE) also interpretation and performance of this method to extract classifiable features from human electroencephalogram (EEG) by using Artificial Neural Networks (ANNs). ANNs are evaluated for accuracy, specificity, and sensitivity on classification of ...

Forecasting is the starting point for drawing good strategies facing the demand variability in the increasingly complex and competitive today's markets. This article discusses two methods of dealing with demand variability in seasonal time series using artificial neural networks (ANN). First a multilayer perceptron model for time series forecasting is proposed. Several learning rules used ...

this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

One of the ways to improve calculations related determining position a node in Iot measurement system is use artificial neural networks (ANN) calculate coordinates. The method described article based on RSSI (Received Signal Strenght Indicator), whitch value then processed by network. Hence, proposed works two stages. In first stage, coefficient samples are taken, and location determined an ong...

Artificial neural networks provide a feasible approach to model complex engineering systems. Computational parallelism is assumed as a basis of the neural architectures. In the Russian Federal Nuclear Center VNIITF there exists a neural simulator Nimfa. In the framework of this project parallel versions of training algorithms for feed-forward neural networks based on the MPI standard are develo...

Job Satisfaction (JS) plays important role as a competitive advantage in organizations especially in helth industry. Recruitment and retention of human resources are persistent problems associated with this field. Most of the researchs have focused on the job satisfaction factors and few of researches have noticed about its effects on productivity. However, little researchs have focused on the ...

Complex networks, like the scale-free model, are observed in many biological and social systems and the application of this topology to artificial neural networks (ANN) leads to interesting considerations. In this paper, we present a preliminary study on the modelling capabilities of ANN with complex topologies. We used an evolutionary algorithm (EA) to train them providing thus the paradigm of...