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Scattering signal extracting using system modelling method based on a back propagation neural network

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2 Author(s)
Liang-Jie Zhang ; Dept. of Inf. & Control Eng., Xi'an Jiaotong Univ., Shaanxi, China ; Wen-Bin Wang

Summary form only given. A neural network called the back-propagation net and its use in learning the impulse response function of a TSRS (transient subsurface radar system) necessary for system modelling were described. Neural network modelling was selected because of the ability to adapt to the environment through training, which makes it possible to avoid many of the problems associated with traditional system modelling methods. Simulations were performed which used experimental data as inputs and desired outputs of the neural networks during the training process. The excitation signals x(n) were used as the inputs of the neural network, and the direct receiving signals y(n) were used as outputs. The neural network learns to solve this problem by being trained on many training sets of pairs (x(n), y(n)). The size and nature of the training set were discussed briefly. The high performance of this neural network modelling in scattering signal extraction processing was shown.<>

Published in:

Antennas and Propagation Society International Symposium, 1992. AP-S. 1992 Digest. Held in Conjuction with: URSI Radio Science Meeting and Nuclear EMP Meeting., IEEE

Date of Conference:

18-25 June 1992