Abstract: In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural network is able to pr...

The main focus in this study is on data pre-processing, reduction in number of inputs or input space size reduction the purpose of which is the justified generalization of data set in smaller dimensions without losing the most significant data. In case the input space is large, the most important input variables can be identified from which insignificant variables are eliminated, or a variable ...

introduction rainfall is considered as one of the most important factures in water cycle. prediction of monthly rainfall is important for many purposes such as estimating torrent, drought, run-off, sediment, irrigation programming and also management of drainage basins. rainfall prediction in each area is mediated by punctual data measured as humidity, temperature, wind speed and etc. as iran i...

typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. the distillation control system, therefore, must hold product compositions as near the set points as possible in faces of upset. in this project, inferential model predictive control, that utilizes an artificial neural network estimator and model predictive cont...

Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile properties of co...

abstract: this paper represents a novel use of artificial neural networks in medical science. the proposed technique involves training a multi layer perceptron (mlp) (a kind of artificial neural network) with a bp learning algorithm to recognize a pattern for the diagnosing and prediction of five blood disorders, through the results of blood tests from h1 machine. the blood test parameters and ...

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...

realizing the difficulties involved in direct measurement of soil properties, in recent years, alternative methods have been employed. in the present research, soil texture, organic carbon, saturation percentage and lime as readily measurable parameters, wilting point, field capacity, cation exchange capacity as well as bulk density, as predicted variables were evaluated. the data set was then ...

Scour in the downstream of hydraulic structures is a phenomenon which usually occurs due to exceeding the velocity or shear stress from a critical level. In this paper by using the laboratory data by Borman- Jouline and De-Agostino research, it was tried to get more accurate equations in order to calculate the maximum depth of scour in the downstream of the water level regulation structures. Co...

this study shows the usefulness of artificial neural network (ann) in maintenance planning and man-agement. an ann model based on the multi-layer perceptron having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. the model achieved an accuracy of over 70% in predicting the expected downtime.