The static pull-in instability of beam-type micro-electromechanical systems is theoretically investigated. Two engineering cases including cantilever and double cantilever micro-beam are considered. Considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, c...

The Go-Back-N and Selective-Repeat automatic repeat request (ARQ) protocols are widely used for error control in computer-communication networks. They are nearly optimal for channels characterized by low error rates and small propagation delays, and thus suitable for many classical data transmission applications. However, these protocols are not eecient for modern non-conventional channels with...

the static pull-in instability of beam-type micro-electromechanical systems is theoretically investigated. two engineering cases including cantilever and double cantilever micro-beam are considered. considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent euler-bernoulli beam model is used based on a modified couple stress theory, c...

This paper presents the performance comparison between back propagation, recursive prediction error (RPE) and modified recursive prediction error (MRPE) algorithms for training multilayered perceptron networks. Back propagation is a steepest descent type algorithm that normally has slow convergence rate and the search for the global minimum often becomes trapped at poor local minima. RPE and MR...

A Back Propagation Artificial Neural Network (BPANN) is a well-known learning algorithmpredicated on a gradient descent method that minimizes the square error involving the networkoutput and the goal of output values. In this study, 261 GPS/Leveling and 8869 gravity intensityvalues of Iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,“BPANN”, and “collocation” ...

A comparative study of artificial neural network (ANN) and multiple regression is made to predict the fat tail weight of Balouchi sheep from birth, weaning and finishing weights. A multilayer feed forward network with back propagation of error learning mechanism was used to predict the sheep body weight. The data (69 records) were randomly divided into two subsets. The first subset is the train...

The supervised learning of the discriminative convolutional neural network (ConvNet or CNN) is powered by back-propagation on the parameters. In this paper, we show that the unsupervised learning of a popular top-down generative ConvNet model with latent continuous factors can be accomplished by a learning algorithm that consists of alternatively performing back-propagation on both the latent f...