基于神经网络的方位估计

ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1988-04-11 DOI:10.1109/ICASSP.1988.197059

S. Jha, R. Chapman, T. Durrani

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引用次数: 15

摘要

对J.J. Hopfield(1982)最初提出的神经网络算法进行了增益退火和迭代下降两种改进，从而更好地收敛到全局最小值。仿真结果说明了所提算法在方位估计中的性能。

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Bearing estimation using neural networks

Two modifications to the neural-network algorithm originally proposed by J.J. Hopfield (1982), gain annealing and iterated descent, are proposed that yield better convergence to the global minimum. Simulation results are presented to illustrate the performance of the proposed algorithm for bearing estimation.<>

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ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing

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