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Cascade-Forward Neural Network for Volterra Integral Equation Solution
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The method of solving volterra integral equation by using numerical solution is a simple operation but to require many memory space to compute and save the operation. The importance of this equation appeares new direction to solve the equation by using new methods to avoid obstacles. One of these methods employ neural network for obtaining the solution.

This paper presents a proposed method by using cascade-forward neural network to simulate volterra integral equations solutions. This method depends on training cascade-forward neural network by inputs which represent the mean of volterra integral equations solutions, the target of cascade-forward neural network is to get the desired output of this network. Cascade-forward neural network is trained multi times to obtain the desired output, the training of cascade-forward neural network model terminal when there is no enhancement in result. The model combines all training cascade-forward neural network to obtain the best result. This method proved its successful in training and testing cascade-forward neural network for obtaining the desired output of numerical solution of volterra integral equation for multi intervals. Cascade-forward neural network model measured by calculating MSE to compute the degree of error at each training time.

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge
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Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology
Water quality assessment and sodium adsorption ratio prediction of Tigris River using artificial neural network
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Publication Date
Mon Jan 01 2024
Journal Name
Itm Web Of Conferences
Embedded Neural Network like PID Water Heating Controller Implementing Cycle by Cycle Power Control Scheme
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This paper experimentally investigates the heating process of a hot water supply using a neural network implementation of a self-tuning PID controller on a microcontroller system. The Particle Swarm Optimization (PSO) algorithm employed in system tuning proved very effective, as it is simple and fast optimization algorithm. The PSO method for the PID parameters is executed on the Matlab platform in order to put these parameters in the real-time digital PID controller, which was experimented with in a pilot study on a microcontroller platform. Instead of the traditional phase angle power control (PAPC) method, the Cycle by Cycle Power Control (CBCPC) method is implemented because it yields better power factor and eliminates harmonics

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Publication Date
Tue May 01 2018
Journal Name
Journal Of Physics: Conference Series
Estimation of Heavy Metals Contamination in the Soil of Zaafaraniya City Using the Neural Network
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Publication Date
Fri Jan 01 2016
Journal Name
Computational Intelligence And Neuroscience
A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)
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This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl

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Publication Date
Sun Nov 03 2002
Journal Name
University Of Baghdad
Parametric Study Of The Optimum Wing - Canard Configuration For The Swept Forward Fighter
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The aerodynamic characteristics of the forward swept wing aircraft have been studied theoretically and an experimentally investigation for the wake field generated by this configuration have been carried out. Low order panel method with the Dirichlet boundary condition have been used to solve the case of the steady, inviscid and compressible flow. Two different panel method techniques have been employed: the source-doublet and the doublet method. The thickness for the various components was considered in the study. Prandtl-Glauert similarity rule has been used to account for the compressibility effects. Experimentally, a model was manufactured from wood with body length (290mm) and main wing span was (204mm). The primary objective of th

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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Advanced Research In Fluid Mechanics And Thermal Sciences
Aerodynamics of a Formula One car front cascade wing during cornering
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Publication Date
Thu Nov 03 2022
Journal Name
Sensors
A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal
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In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

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Publication Date
Fri Mar 31 2017
Journal Name
Al-khwarizmi Engineering Journal
Comparative Study for Organic and Inorganic Draw Solutions in Forward Osmosis
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The present work aims to study forward osmosis process using different kinds of draw solutions and membranes. Three types of draw solutions (sodium chloride, sodium formate, and sodium acetate) were used in forward osmosis process to evaluate their effectiveness with respect to water flux and reverse salt flux. Experiments conducted in a laboratory-scale forward osmosis (FO) unit in cross flow flat sheet membrane cell.  Three types of membranes (Thin film composite (TFC), Cellulose acetate (CA), and Cellulose triacetate (CTA)) were used to determine the water flux under osmotic pressure as a driving force. The effect of temperature, draw solution concentration, feed and draw solution flow rate, and membrane types, were studied with

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Publication Date
Tue Jun 18 2024
Journal Name
2024 Ieee 33rd International Symposium On Industrial Electronics (isie)
An Adaptive Integral Sliding Mode Control for Disturbed Servo Motor Systems
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Abstract-Servo motors are important parts of industry automation due to their several advantages such as cost and energy efficiency, simple design, and flexibility. However, the position control of the servo motor is a difficult task because of different factors of external disturbances, nonlinearities, and uncertainties. To tackle these challenges, an adaptive integral sliding mode control (AISMC) is proposed, in which a novel bidirectional adaptive law is constructed to reduce the control chattering. The proposed control has three steps to be designed. Firstly, a full-order integral sliding manifold is designed to improve the servo motor position tracking performance, in which the reaching phase is eliminated to achieve the invariance of

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