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electromagnetic tomography, as a nondestructive method, is employed. It is a difficult goal
to produce precise electromagnetic tomography images using analytical methods. To cope
with this issue, an artificial neural network is trained to mimic the electromagnetic tomography
system. A hybrid optimization algorithm, which is a combination of intelligent global
harmony search and Levenberg–Marquardt algorithms, is proposed to optimize the artificial
neural network weights and biases. Simulation results show that the proposed method can
estimate the position of target with an acceptable accurately.
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TABLE 2 SSE for IGHSþLMA, IGHS, LMA, and GA
ANN method SSE test
IGHSþLMA 0.170416
IGHS 0.261249
LMA 0.238957
GA 0.776442
NONDESTRUCTIVE POSITION 189
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