A total of 1099 data points consisting of alcohol-alcohol, alcohol-alkane, alkane-alkane, alcohol-amine and acid-acid binary solutions were collected from scientific literature to develop an appropriate artificial neural network (ANN) model. Temperature, molecular weight of the pure components, mole fraction of one component and the structural groups of the components were used as input paramet...

Infiltration rate is one of the most important parameters used in irrigation water management. Direct measurement of infiltration process is laborious, time consuming and expensive. Therefore, in this study application of some indirect methods such as artificial neural networks (ANNs) for prediction of this phenomenon was investigated. Different ANNs structures including two training algorithms...

in this study, a three–layer artificial neural network (ann) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. a total of 455 data points were collected from 13 data sources to develop the ann model. superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of t...

The number of people with disabilities is increasing, so it requires bionic devices to replace human motor functions. Brain-Computer Interface (BCI) can be a tool for the device communicate brain. Signal brain or Electroencephalogram (EEG) signal need classify drive corresponding device. This research goal imagination right and left-hand movements based on EEG signal. system design in this cons...

An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer....

abstract cation exchange capacity (cec) is an important characteristic of soil in terms of nutrient and water holding capacities and contamination management. measurement of cec is laborious and time-consuming. therefore, cec estimation through other easily - measured properties is desirable. in this study, ptfs for estimation of cation exchange capacity from basic soil properties such as parti...

‎Rough extreme learning machines (RELMs) are rough-neural networks with one hidden layer where the parameters between the inputs and hidden neurons are arbitrarily chosen and never updated‎. ‎In this paper‎, ‎we propose RELMs with a stable online learning algorithm for the identification of continuous-time nonlinear systems in the presence of noises and uncertainties‎, ‎and we prove the global ...

in recent years, artificial neural networks (anns) have been widely used for flood esti-mation. in this study, an ann model based on the geomorphologic characteristics of a watershed such as the number of possible paths and their probabilities is developed (gann model). nodes in the input layer are allocated to the surface flows, subsurface flows, excess-rainfall and infiltrated rain. the numbe...

We employ both master equation and order parameter approaches to analyze the asymptotic dynamics of on-line learning with different learning rate annealing schedules. We examine the relations between the results obtained by the two approaches and obtain new results on the optimal decay coefficients and their dependence on the number of hidden nodes in a two layer architecture.