in this study, an artificial neural network was developed in order to analyze flexible pavement structure anddetermine its critical responses under the influence of standard axle loading. in doing so, more than 10000four-layered flexible pavement sections composed of asphalt concrete layer, base layer, subbase layer, andsubgrade soil were analyzed under the impact of standard axle loading. pave...

Metaheuristic algorithm such as BAT algorithm is becoming a popular method in solving many hard optimization problems. This paper investigates the use of Bat algorithm in combination with Back-propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory and to increase the convergence rate. The performance of the proposed Bat based Back-Propagatio...

A large fraction of recent work in artificial neural nets uses multilayer perceptrons trained with the back-propagation algorithm described by Rumelhart et. a1. This algorithm converges slowly for large or complex problems such as speech recognition, where thousands of iterations may be needed for convergence even with small data sets. In this paper, we show that training multilayer perceptrons...

Demands on numerical integration algorithms for astrodynamics applications continue to increase. Common methods, like explicit Runge-Kutta, meet the orbit propagation needs of most scenarios, but more specialized scenarios require new techniques to meet both computational efficiency and accuracy needs. This paper provides an extensive survey on the application of symplectic and collocation meth...

The objective of this research is to construct parallel models that simulate the behavior of artificial neural networks. The type of network that is simulated in this project is the counter – propagation network and the parallel platform used to simulate that network is the message passing interface (MPI). In the next sections the counter – propagation algorithm is presented in its serial as we...

The uncertainty propagation of composite structures is investigated considering descriptive statistical measures of the response variability. A study based on sensitivity to uncertainty that allows selecting the important parameters using sensitivity indices is presented. The uncertainty propagation and the importance measure of input parameters are analysed using four different approaches: a f...

An identification algorithm for vibrating dynamic characterization by using artificial neural network is developed for multi-degree-of freedom systems. The over-fitting problem of classical back-propagation algorithm during neural network training is solved by using regularization procedure with regularized objective function. The practical application shows that the proposed training method is...

This paper presents a new approach to equalization of communication channels using Artificial Neural Networks (ANNs). A novel method of training the ANNs using Tabu based Back Propagation (TBBP) Algorithm is described. The algorithm uses the Tabu Search (TS) to improve the performance of the equalizer as it searches for global minima which is many a time escaped while Back Propagation (BP) algo...