Lec. Dr. Mohammed Moanes Ezzaldean Ali; Assist. Prof. Dr. Hanan A. R. Akkar; Prof. Dr. A.K.M. AL-Shaikhli
Volume 10, Issue 1 , December 2010, , Page 56-71
Abstract
Abstract:
Induction heating system has a number of inherent benefits compared to traditional heating systems. Many analytical and numerical approaches have been applied to solve the problem of induction heating. Artificial Neural Networks possess many advantages and have the ability to tackle problems ...
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Abstract:
Induction heating system has a number of inherent benefits compared to traditional heating systems. Many analytical and numerical approaches have been applied to solve the problem of induction heating. Artificial Neural Networks possess many advantages and have the ability to tackle problems that cannot be accomplished by more analytical and numerical methods. This paper involves modeling many artificial neural networks, and training them based on the results of analysis induction heating systems, by using ANSYS package, to enable them to evaluate the heat distribution inside the workpiece of any induction heating system. Also neural networks are used to specify the time and the power supply required for any desired heat distribution inside the workpiece. The neural networks are simulated by using Neural Network Toolbox in MATLAB, and the networks are trained according to supervised scaled conjugate gradient algorithm until the performance function (mean square error) reach the goal (=10-4). Artificial Neural Networks show a good success in solving the problem of induction heating through obtaining results with high accuracy and very short run time.
Walid A .mahmoud; Munther h; Muthana h
Volume 5, Issue 2 , December 2005, , Page 1-17
Abstract
Abstract:
A single layer feed-forward neural network are proposed and implemented using the
schematic editor of the Xilinx foundation series 2.1i. First the mathematical model of the
data set (weights and inputs) is presented in a matrix multiplication format. Secondly the
five design stages are ...
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Abstract:
A single layer feed-forward neural network are proposed and implemented using the
schematic editor of the Xilinx foundation series 2.1i. First the mathematical model of the
data set (weights and inputs) is presented in a matrix multiplication format. Secondly the
five design stages are presented and implemented without using the finite state machine,
which control the processes of the forward propagation phase, error calculation, and the
training algorithm. Finally the design can be optimized to decrease the total execution time
and to minimize the cost, which eventually will increase the performance and improve the
function density.
