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An evaluation method of uncertainty in the entropy measurement for multisensor neural network of poor information

A measurement uncertainty, multi-sensor technology, applied in instruments, special data processing applications, electrical digital data processing, etc., can solve problems such as inaccuracy

Inactive Publication Date: 2014-08-27
BEIHANG UNIV
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Problems solved by technology

It is not accurate to keep assuming that the distribution is known exactly

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  • An evaluation method of uncertainty in the entropy measurement for multisensor neural network of poor information
  • An evaluation method of uncertainty in the entropy measurement for multisensor neural network of poor information
  • An evaluation method of uncertainty in the entropy measurement for multisensor neural network of poor information

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Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation examples.

[0046] The invention proposes an information-poor multi-sensor neural network-entropy measurement uncertainty evaluation method for the information-poor multi-sensor measurement data with small measurement data sample size and unknown measurement data probability distribution.

[0047] one sight figure 1 , a kind of lack of information multi-sensor neural network-entropy measurement uncertainty evaluation method of the present invention, it comprises the following steps:

[0048] (1) Multi-sensor data fusion;

[0049] (2) Multi-sensor maximum entropy evaluation;

[0050] (3) True value and interval estimation.

[0051] 1. Multi-sensor data fusion

[0052] Through the non-parametric measurement model based on RBF neural network, the center of the basis function is determined by using the gray clustering results of training sample...

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Abstract

The invention relates to an evaluation method of uncertainty in the entropy measurement for multisensor neural network of poor information, and belongs to measuring and testing field. The method is characterized in following steps:(1)using RBF(Radial Basis Function) to establish a data fusion model of multisensor data conditions under the condition of poor information, acquiring fusion sequence reflecting the measuring process;(2)using the maximum entropy principle to process obtained fused sequence, to obtain the probability density function of the fusion sequence;(3)using obtained probability density function to process true value and regional evaluation, to realize uncertain measurement with poor information. The method allows small sample of measure data, the unknown probability distribution and can make full use of the existing information of measured data to realize effective evaluation of uncertain measurement.

Description

technical field [0001] The invention belongs to the field of metrology and testing, and relates to an information-poor multi-sensor neural network-entropy measurement uncertainty evaluation method. Background technique [0002] The evaluation of multi-sensor measurement uncertainty includes two key technologies: one is multi-sensor data fusion technology; the other is effective evaluation technology of fusion sequence uncertainty. Data fusion technology refers to organically fusing the measurement information of multiple sensors in data processing to estimate the measurement results more reasonably. The effective evaluation technique of uncertainty refers to making full use of existing information to evaluate the measurement uncertainty of measurement data. These two key technologies are widely used in metrology and testing, precision instruments, aerospace, environmental monitoring and other fields. [0003] Data fusion technology has become a key technology that has attr...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 王中宇姚贞建王倩
Owner BEIHANG UNIV
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