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A Harmonic Retrieval Algorithm with Neural Computation
Mingyoung ZHOU Jiro OKAMOTO Kazumi YAMASHITA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1992/09/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
hopfield's neural network, neural computation, frequency retrieval, optimal projection,
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A novel harmonic retrieval algorithm is proposed in this paper based on Hopfield's neural network. Frequencies can be retrieved with high accuracy and high resolution under low signal to noise ratio (SNR). Amplitudes and phases in harmonic signals can also be estimated roughly by an energy constrained linear projection approach as proposed in the algorithm. Only no less than 2q neurons are necessary in order to detect harmonic siglnals with q different frequencies, where q denotes the number of different frequencies in harmonic signals. Experimental simulations show fast convergence and stable solution in spite of low signal to noise ratio can be obtained using the proposed algorithm.