In this paper, vapor pressure for pure compounds is estimated using the Artificial Neural Networks and a simple Group Contribution Method (ANN–GCM). For model comprehensiveness, materials were chosen from various families. Most of materials are from 12 families. Vapor pressure data of 100 compounds is used to train, validate and test the ANN-GCM model. Va...

One of the features of wireless sensor networks is that the nodes in this network have limited power sources. Therefore, assessment of energy consumption in these networks is very important. What has been common practice has been the use of traditional simulators to evaluate the energy consumption of the nodes in these networks. Simulators often have problems such as fluctuating output values i...

Accurate and parsimonious approximations for indicator functions of d-dimensional balls and related functions are given using level sets associated with the thresholding of a linear combination of ramp sigmoid activation functions. In neural network terminology, we are using a single-hidden-layer perceptron network implementing the ramp sigmoid activation function to approximate the indicator o...

Multi-layer feedforward neural networks with sigmoidal activation functions have been termed "universal function approximators". Although these types of networks can approximate any continuous function to a desired degree of accuracy, this approximation may require an inordinate number of hidden nodes and is only accurate over a finite interval. These short comings are due to the standard multi...

A three-layer artificial neural network (ANN) model was developed to predict the remained DO (deoxygenation) in water after DO removal with an enzymatic granular biocatalyst (GB), based on the experimental data obtained in a laboratory stirring batch study. The effects of operational parameters such as initial pH, initial glucose concentration and temperature on DO removal were investigated. On...

proper translation by a user without any expert knowledge of how the computer stores and represents rules. This paper demonstrates the utility of neural networks in precisely this area on a small scale translation problem. We have tested the ability of neural networks to perform natural language translation. Our results have shown a greatly improved translation accuracy in comparison to the wor...

In this paper we are discussing the specific problems that occur in the MAC layer for WLAN's with beamforming, such as hidden nodes, neighbor discovery, setting beamforming coefficients in MIMO systems and training sequences. We compare the solutions in MAC protocols based on different extensions of the IEEE 802.11 standard, on HIPERLAN/2 and in the MIMAX project.