A Harmonic Retrieval Algorithm with Neural Computation

Mingyoung ZHOU  Jiro OKAMOTO  Kazumi YAMASHITA  

IEICE TRANSACTIONS on Information and Systems   Vol.E75-D   No.5   pp.718-727
Publication Date: 1992/09/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Bio-Cybernetics
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.