Hopfield neural network for AR spectral estimator

Research output: Contribution to journalConference article

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An autoregressive (AR) spectrum estimator using the Hopfield neural network (HNN) is introduced. The HNN is designed to minimize the mean squared error between a subject signal and the assumed AR model of the signal. The output of the HNN is the estimated AR coefficients; thus, the spectrum of the signal can be directly obtained in terms of the AR coefficients and the sampling interval. An odd symmetric soft-limiter-type neuron is selected for the HNN rather than the sigmoid or the hard limiter type. The idea is extended for the bearing estimation problem for sonar and radar. A hardware implementation of the model is discussed.

Original languageEnglish
Pages (from-to)487-490
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Publication statusPublished - 1990 Dec 1
Event1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4) - New Orleans, LA, USA
Duration: 1990 May 11990 May 3


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