Mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF

A TE-ANN-AWF, error compensation technology, applied in the field of telemetry error compensation based on TE-ANN-AWF mobile pollution source, can solve the problem of remote sensing detection method being easily interfered by the external environment, etc., to improve the applicability and anti-interference ability , to ensure the effect of long-term stability

Active Publication Date: 2018-09-28
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0005] The present invention aims at the problem that the remote sensing detection method of mobile pollution sources is easily disturbed by the external environment. In this paper, a new error compensation model TE-ANN- AWF

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  • Mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF
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  • Mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF

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Embodiment Construction

[0042] In order to make the technological innovation realized by the present invention easy to understand, the following is combined figure 1 To further describe the implementation of the present invention in detail, the specific steps are as follows:

[0043] Step 1: Obtain measurement samples under different interference effects through environmental simulation experiments, and then preprocess and normalize the measurement samples. Based on the experimental measurement samples, the interference correlation analysis is carried out through TE transfer entropy, so as to determine the source of measurement error and measure the degree of imbalance between multiple interferences. And use the directionality of TE transfer entropy to derive the quantitative standard and judgment method of non-significant causality.

[0044] Suppose Xn and Yn are two environmental disturbance change sequences and remote sensing measurement observation sequences with discrete states of xn and yn at time n...

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Abstract

The invention discloses a mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF. The TE transfer entropy is used to perform a causal correlation analysis on interference and measure results, an error source is determined and a multi-interference imbalance degree is measured, and the directivity of the TE transfer entropy is used for leading out a quantification standard and a determination method having a non-substantial causal correlation. A virtual observation method is provided for realizing multiple deconstruction of a unit observation sequence, an ANN error prediction model is used to realize compensation of a single interference channel virtual observation sequence, and a multiple adaptive weight fusion method is employed for fusion reconstruction on a multiple virtual observation sequence. By aiming at a weight convergence problem in a fusion algorithm, an index forgetting method is introduced in a model to combine the good weight pre-estimatedcapability of TE and the weight adaptive adjustment of AWF, and the dynamic performance of a error compensation process is improved.

Description

Technical field [0001] The present invention designs a TE-ANN-AWF-based mobile pollution source remote sensing detection error compensation method, which belongs to the technical field of error compensation for mobile pollution source remote sensing detection instruments, and aims to correct the measurement results of remote sensing detection instruments under external environmental interference According to the entropy estimation theory, adaptive fusion and neural network related theories, compensation and estimation are carried out to solve the problem of remote sensing detection method being vulnerable to external environmental interference. Background technique [0002] Mobile pollution sources refer to sources that do not emit air pollutants through stationary equipment, such as motor vehicles, mobile construction machinery, ships and airplanes that emit exhaust gas during movement. Mobile source pollution, especially heavy-duty diesel trucks, commercial gasoline vehicles, o...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/17G06F17/18G06N3/08
CPCG01N21/17G01N2021/1795G06F17/18G06N3/08
Inventor 蒋鹏华通席旭刚佘青山甘海涛
Owner HANGZHOU DIANZI UNIV
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