Prediction of methanol loss by hydrocarbon gas phase in hydrate inhibition unit by back propagation neural networks

modeling loss data by phase-type distribution

بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...

On the use of back propagation and radial basis function neural networks in surface roughness prediction

Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...

Nyquist Plots Prediction Using Neural Networks in Corrosion Inhibition of Steel by Schiff Base

The corrosion inhibition effect of N,N′-bis(n-Hydroxybenzaldehyde)-1,3-Propandiimine on mild steel has been investigated in 1 M HCl using electrochemical impedance spectroscopy. A predictive model was presented for Nyquist plots using an artificial neural network. The proposed model predicted the imaginary impedance based on the real part of the impedance as a function of time. The model to...

Prediction of Gas Hydrate Forming Pressures by Using PR Equation of State and Different Mixing Rules

Prediction of Gas Lift Parameters Using Artificial Neural Networks

Back - Propagation Neural Networks for

A back-propagation neural network is applied to a nonlinear self-tuning tracking problem. Traditional self-tuning adaptive control techniques can only deal with linear systems or some special nonlinear systems. The emerging back-propagation neural networks have the capability to learn arbitrary nonlinearity and show great potential for adaptive control applications. A scheme for combining back-...