this paper employs a multivariate dynamic conditional correlation garch model, which is developed by engle (2001, 2002), to detect the timing and nature of changes in the comovement between iranian output and prices for the periods after iran–iraq war , known as imposed war . the results showed that there is a weak correlation between output and prices after imposed war and  varies periodically...

This paper proposes two model predictive control (MPC) methods for a Hammerstein with both unmeasurable state and bounded disturbance. First, dynamic output feedback MPC is designed such that the dynamics of estimation error decoupled from estimated state. Hence, separation principle holds part controller parameters can be off-line. Second, case considered where nonlinearity not exactly inverte...

Memory properties of fractional-order operators are considered for an input-output data model highly uncertain nonlinear systems. The arises by relating the variation output to input; instantaneous gain is computed through a fuzzy inference network, whose consequences adapted online on gradient descent rule. nature proposed relaxes stringent conditions data-driven schemes, allowing changes in s...

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 ...