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...

In this paper, 2D seismic data and petrophysical logs of the Pabdeh Formation from four wells of the Mansuri oil field are utilized. ΔLog R method was used to generate a continuous TOC log from petrophysical data. The calculated TOC values by ΔLog R method, used for a multi-attribute seismic analysis. In this study, seismic inversion was performed based on neural networks algorithm and the resu...

Dynamic neural networks are often used for nonlinear system identification. This paper presents a novel series-parallel dynamic neural network structure which is suitable for nonlinear system identification. A theoretical proof is given showing that this type of dynamic neural network is able to approximate finite trajectories of nonlinear dynamical systems. Also, this neural network is trained...

Dynamic neural networks are often used for nonlinear system identification. This paper presents a novel series-parallel dynamic neural network structure which is suitable for nonlinear system identification. A theoretical proof is given showing that this type of dynamic neural network is able to approximate finite trajectories of nonlinear dynamical systems. Also, this neural network is trained...

Abstract A class of recurrent neural networks is developed to solve nonlinear equations, which are approximated by a multilayer perceptron (MLP). The recurrent network includes a linear Hopfield network (LHN) and the MLP as building blocks. This network inverts the original MLP using constrained linear optimization and Newton’s method for nonlinear systems. The solution of a nonlinear equation ...

we establish a relationship between general constrained pseudoconvex optimization problems and globally projected dynamical systems. a corresponding novel neural network model, which is globally convergent and stable in the sense of lyapunov, is proposed. both theoretical and numerical approaches are considered. numerical simulations for three constrained nonlinear optimization problems are giv...

‎‎in the data envelopment analysis (dea) the efficiency of the units‎ ‎can be obtained by identifying the degree of the importance of the‎ ‎criteria (inputs-outputs).in dea basic models‎, ‎challenges are zero‎ ‎and unequal weights of the criteria when decision‎- ‎making units are‎ ‎evaluated‎. ‎one of the strategies applied to deal with these problems‎ ‎is using common weights of the each input...

runoff is one of the major components of calculating water resource processes and is the main issue in hydrology. many concept models are used to predict the amount of runoff, which in most cases depend on topographical and hydrological data. conventional models are not appropriate for areas in which there is little hydrological data. changes in runoff are nonlinear, meaning it is time & space ...