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Title: | Latency probability estimation of non-linear systems with one-step randomly delayed measurements |

Authors: | Yulong Huang;Yonggang Zhang;Ning Li |

subject: | unknown constant latency probability estimation algorithm|Newton approach|M-step|expectation maximisation approach|maximisation step|univariate nonstationary growth model|maximum likelihood criterion|complete data log-likelihood function|ML criterion|one-step randomly delayed measurements|E-step|nonlinear systems|expectation step |

Year: | 2016 |

Publisher: | IEEE |

Abstract: | In this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectation maximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E-step) and the maximisation step (M-step). In the E-step, the expectation of the complete data log-likelihood function is approximately computed based on the currently estimated latency probability, and in the M-step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non-stationary growth model. |

URI: | http://localhost/handle/Hannan/147822 http://localhost/handle/Hannan/602743 |

ISSN: | 1751-8644 1751-8652 |

volume: | 10 |

issue: | 7 |

Appears in Collections: | 2016 |

Files in This Item:

File | Description | Size | Format | |
---|---|---|---|---|

7449097.pdf | 901.3 kB | Adobe PDF | Preview File |

Title: | Latency probability estimation of non-linear systems with one-step randomly delayed measurements |

Authors: | Yulong Huang;Yonggang Zhang;Ning Li |

subject: | unknown constant latency probability estimation algorithm|Newton approach|M-step|expectation maximisation approach|maximisation step|univariate nonstationary growth model|maximum likelihood criterion|complete data log-likelihood function|ML criterion|one-step randomly delayed measurements|E-step|nonlinear systems|expectation step |

Year: | 2016 |

Publisher: | IEEE |

Abstract: | In this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectation maximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E-step) and the maximisation step (M-step). In the E-step, the expectation of the complete data log-likelihood function is approximately computed based on the currently estimated latency probability, and in the M-step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non-stationary growth model. |

URI: | http://localhost/handle/Hannan/147822 http://localhost/handle/Hannan/602743 |

ISSN: | 1751-8644 1751-8652 |

volume: | 10 |

issue: | 7 |

Appears in Collections: | 2016 |

Files in This Item:

File | Description | Size | Format | |
---|---|---|---|---|

7449097.pdf | 901.3 kB | Adobe PDF | Preview File |

Title: | Latency probability estimation of non-linear systems with one-step randomly delayed measurements |

Authors: | Yulong Huang;Yonggang Zhang;Ning Li |

subject: | unknown constant latency probability estimation algorithm|Newton approach|M-step|expectation maximisation approach|maximisation step|univariate nonstationary growth model|maximum likelihood criterion|complete data log-likelihood function|ML criterion|one-step randomly delayed measurements|E-step|nonlinear systems|expectation step |

Year: | 2016 |

Publisher: | IEEE |

Abstract: | In this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectation maximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E-step) and the maximisation step (M-step). In the E-step, the expectation of the complete data log-likelihood function is approximately computed based on the currently estimated latency probability, and in the M-step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non-stationary growth model. |

URI: | http://localhost/handle/Hannan/147822 http://localhost/handle/Hannan/602743 |

ISSN: | 1751-8644 1751-8652 |

volume: | 10 |

issue: | 7 |

Appears in Collections: | 2016 |

Files in This Item:

File | Description | Size | Format | |
---|---|---|---|---|

7449097.pdf | 901.3 kB | Adobe PDF | Preview File |