### Abstract

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 language | English |
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Pages (from-to) | 487-490 |

Number of pages | 4 |

Journal | Proceedings - IEEE International Symposium on Circuits and Systems |

Volume | 1 |

Publication status | Published - 1990 Dec 1 |

Event | 1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4) - New Orleans, LA, USA Duration: 1990 May 1 → 1990 May 3 |