This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) architecture and feed-forward neural networks (FFNN) for learning the solution of nonlinear partial differential equations (PDEs). The aims at incorporating both grid input representations using two streams corresponding to GCN FFNN models, respectively. Each stream layer receives processes its repres...

important parameters on apple drying process are investigated experimentally and modeled employing artificial neural network and neuro-taguchi's method. experimental results show that the apple drying curve stands in the falling rate period of drying. temperature is the most important parameter that has a more pronounced effect on drying rate than the other two parameters i.e. air velocity and ...

This study focuses on using an artificial neural network (ANN) based model for predicting the performance of high strength concrete (HSC) beams strengthened with surface mounted FRP laminates. Eight input parameters such as geometrical properties of the beam and mechanical properties of FRP laminates were considered for this study. Back propagation network with Lavenberg-Marquardt algorithm has...

Feed Forward Neural Networks (FFNNs) are computational techniques inspired by the physiology of the brain and used in the approximation of general mappings from one nite dimensional space to another. They present a practical application of the theoretical resolution of Hilbert's 13 th problem by Kolmogorov and Lorenz, and have been used with success in a variety of applications. However, as the...

Abstract Preventing plunge pool scouring in hydraulic structures is crucial engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets several fields, available equations deficiencies calculating exact due complexity of process. This study investigated local using Metaheuristic Artificial Bee Colony-Optimized Feed Forwa...

In this paper, we present a machine vision system that was developed on the basis of neural networks to identify twelve houseplants. Image processing system was used to extract 41 features of color, texture and shape from the images taken from front and back of the leaves. The features were fed into the neural network system as the recognition criteria and inputs. Multilayer perceptron (MLP) ne...

in this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. in order to eliminate the effect of these uncertain...

in this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (lims). dynamical equations of lim have been considered accurate. in other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. then equations of the reference model and adaptive mode...

the ability of artificial neural network (ann) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real  concern. as the most applicable network, the ann with multi-layer back propagation perceptrons is used to approximate functions. throughout the current work, the daily effective temperature is determined, and then the weather data w...