This study presents a new procedure based on Artificial Neural Network (ANN) for identification of double cracks in Functionally Graded Beams (FGBs). A cantilever beam is modeled using Finite Element Method (FEM) for analyzing a double-cracked FGB and evaluation of its first four natural frequencies for different cracks depths and locations. The obtained FEM results are verified against availab...

Accurate demand forecasting is one of the most key issues in inventory management of spare parts. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of the forecasting methods may perform poorly whe...

We show that a neural approach to the task of non-factoid answer reranking can benefit from the inclusion of tried-and-tested handcrafted features. We present a novel neural network architecture based on a combination of recurrent neural networks that are used to encode questions and answers, and a multilayer perceptron. We show how this approach can be combined with additional features, in par...

The purpose of train detection systems is to check that related track section clear vehicles before a may be authorized pass through railroad. the important task for ensuring safety traffic. Multi-layer Perceptron classifier, which consists feedforward neural networks constructed multiple layers interconnected artificial neurons, proved effective trainset class classification in this study. Usi...

h this paper, we prove that the online gradient method for continuous perceptrons converges in finite steps when the training patterns are linearly separable. @ 2003 Elsevier Ltd. All rights reserved. Neural networks have been widely used for solving supervised classification problems. In this paper, we consider the simplest feedforword neural network-the perceptron made up of m input neurons a...

Deterministic neural networks such as backpropagation of error, multilayer perceptrons, and locally based radial basis methods have been a major focus of the neural network community in recent years. However, there has been a distinct, albeit less pronounced, interest in stochastic neural networks. In this review we provide the reader with a sense of the defining components of a stochastic neur...

Finding suitable jobs for US Navy sailors periodically is an important and everchanging process. An Intelligent Distribution Agent (IDA) and particularly its constraint satisfaction module take up the challenge to automate the process. The constraint satisfaction module's main task is to provide the bulk of the decision making process in assigning sailors to new jobs in order to maximize Navy a...

Various theoretical results show that learning in conventional feedforward neural networks such as multilayer perceptrons is NP-complete. In this paper we show that learning in min-max modular (M3) neural networks is tractable. The key to coping with NP-complete problems in M3 networks is to decompose a large-scale problem into a number of manageable, independent subproblems and to make the lea...