Publication detail

Improvements of Analog Neural Networks Based on Kalman Filter

TOBEŠ, Z., RAIDA, Z.

Original Title

Improvements of Analog Neural Networks Based on Kalman Filter

Type

journal article - other

Language

English

Original Abstract

In the paper, original improvements of recurrent analog neural networks, which are based on Kalman filter, are presented. These improvements eliminate some disadvantages of the classical Kalman neural network and enable a real time processing of quickly changing signals, which appear in adaptive antennas and similar applications. This goal is reached using such circuit elements, which increase the convergence rate of the network and decrease the dependence of convergence rate on the ratio of eigenvalues of the correlation matrix of input signals.

Keywords

Kalman filter, analog recurrent neural networks, convergence rate, stability

Authors

TOBEŠ, Z., RAIDA, Z.

RIV year

2002

Released

1. 4. 2002

ISBN

1210-2512

Periodical

Radioengineering

Year of study

11

Number

3

State

Czech Republic

Pages from

6

Pages to

13

Pages count

8

BibTex

@article{BUT40903,
  author="Zdeněk {Tobeš} and Zbyněk {Raida}",
  title="Improvements of Analog Neural Networks Based on Kalman Filter",
  journal="Radioengineering",
  year="2002",
  volume="11",
  number="3",
  pages="8",
  issn="1210-2512"
}