this paper presents the application of three main artificial neural networks (anns) in damage detection of steel bridges. this method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. the changes in structural response is used to identify the states of structural damage. to circumvent the difficulty arising from the non-linear n...

The goal of this paper is to compare and analyze the forecasting performance of two artificial neural network models (i.e., MLP (multi-layer perceptron) and DNN (deep neural network)), and to conduct an experimental investigation by data flow, not economic flow. In this paper, we investigate beyond the scope of simple predictions, and conduct research based on the merits and data of each model,...

runoff estimation is one of the main challenges encountered in water and watershed management. spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. artificial neural network (ann) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. these networks can...

This article presents numerical studies on semi-active seismic response control of structures equipped with Magneto-Rheological (MR) dampers. A multi-layer artificial neural network (ANN) was employed to mitigate the influence of time delay, This ANN was trained using data from the El-Centro earthquake. The inputs of ANN are the seismic responses of the structure in the current step, and the ou...

In this paper, the application of neural networks to study the maximum and minimum relative humidity for Chandigarh city is explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model forecasting system is used and Back Propagation algorithm is used to train the network. The proposed network is trained with actual data of the past 10 years (2000-2010) and...

The aim of our study is to propose an approach for transfer function placental development using ultrasound images. This approach is based to the selection of tissues, feature extraction by discrete cosine transform DCT, discrete wavelet transform DWT and classification of different grades of placenta by artificial neural network and especially the multi layer perceptron MLP. The proposed appro...

global positioning system (gps) signals provide valuable information about ionosphere physical structure. using these signals, can be derived total electron content (tec) for each line of sight between the receiver and the satellite. for historic and other sparse data sets, the reconstruction of tec images is often performed using multivariate interpolation techniques. recently it has become cl...

Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robo...

Artificial neural network models have been studied for many years with the hope of designing information proeessing systems to produee human-like performance. The present artiele provides an introduction to neural computing by reviewing three commonly used models (namely, Hopfield's model, Kobonen's model and the Multi-layer perceptron) and showing their applications in various fields such as p...