### Abstract

In this paper an algorithm for decentralized estimation of parameters in linear discrete-time regression models is proposed in the form of a combination of local stochastic approximation algorithms and a global consensus strategy. A rigorous analysis of the asymptotic properties of the proposed algorithm is presented, taking into account both the multi-agent network structure and the probabilities of local measurements and communication faults. In the case of non-vanishing gains in the stochastic approximation algorithms, an upper bound of the mean-square estimation error matrix is defined as a solution of a Lyapunov-like matrix equation, while in the case of asymptotically vanishing gains the mean-square convergence is proved. It is also demonstrated how the consensus strategy can contribute to the reduction of measurement noise influence.

Original language | English (US) |
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Title of host publication | Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC |

Pages | 1535-1540 |

Number of pages | 6 |

DOIs | |

State | Published - Dec 1 2007 |

Event | 46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States Duration: Dec 12 2007 → Dec 14 2007 |

### Publication series

Name | Proceedings of the IEEE Conference on Decision and Control |
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ISSN (Print) | 0191-2216 |

### Other

Other | 46th IEEE Conference on Decision and Control 2007, CDC |
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Country | United States |

City | New Orleans, LA |

Period | 12/12/07 → 12/14/07 |

### Fingerprint

### Keywords

- Consensus strategy
- Convergence analysis
- Decentralized estimation
- Denoising
- Multi-agent systems
- Stochastic approximation

### ASJC Scopus subject areas

- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization

### Cite this

*Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC*(pp. 1535-1540). [4434812] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2007.4434812