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Sea clutter optimal soft-sensing instrument and method based on RBF fuzzy neural network optimized by fruit fly optimization algorithm

A technology of fuzzy neural network and fruit fly optimization algorithm, which is applied in the field of sea clutter optimal soft measurement instrument, can solve the problems of low sensitivity to noise, low measurement accuracy, and poor generalization performance

Inactive Publication Date: 2018-04-20
ZHEJIANG UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of existing radars such as low measurement accuracy, low sensitivity to noise, and poor generalization performance, the present invention provides an on-line measurement, fast calculation speed, automatic model update, strong anti-noise ability, and good generalization performance. Fruit fly optimization algorithm to optimize RBF fuzzy neural network sea clutter optimal soft sensor instrument and method

Method used

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  • Sea clutter optimal soft-sensing instrument and method based on RBF fuzzy neural network optimized by fruit fly optimization algorithm
  • Sea clutter optimal soft-sensing instrument and method based on RBF fuzzy neural network optimized by fruit fly optimization algorithm
  • Sea clutter optimal soft-sensing instrument and method based on RBF fuzzy neural network optimized by fruit fly optimization algorithm

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Embodiment 1

[0082] refer to figure 1 , figure 2 and image 3 , an optimal soft measuring instrument for sea clutter based on fruit fly optimization algorithm to optimize RBF fuzzy neural network, including radar 1, on-site intelligent instrument 2 for measuring easy-to-measure variables, control station 3 for measuring operating variables, storage The on-site database 4 of data and the sea clutter soft measurement value display instrument 6, the on-site intelligent instrument 2, the control station 3 are connected to the radar 1, the on-site intelligent instrument 2, the control station 3 are connected to the on-site database 4, the software The measuring instrument also includes the optimal soft sensor host computer 5 for optimizing the RBF fuzzy neural network with the fruit fly optimization algorithm. The terminal is connected, and the output terminal of the optimal soft sensor host computer 5 based on the fruit fly optimization algorithm to optimize the RBF fuzzy neural network is ...

Embodiment 2

[0118] refer to figure 1 , figure 2 and image 3 , a sea clutter optimal soft sensor method based on fruit fly optimization algorithm to optimize RBF fuzzy neural network, said soft sensor method comprises the following steps:

[0119] 1) For the radar object, according to the process analysis and operation analysis, select the operational variables and easily measurable variables as the input of the model, and the operational variables and easily measurable variables are obtained from the on-site database;

[0120] 2) Preprocess the model training samples input from the on-site database, and centralize the training samples, that is, subtract the average value of the samples, and then standardize them so that the mean value is 0 and the variance is 1. This processing is accomplished using the following algorithmic procedure:

[0121] 2.1) Calculate the mean:

[0122] 2.2) Calculate the variance:

[0123] 2.3) Standardization:

[0124] Among them, TX is the trainin...

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Abstract

The invention discloses a sea clutter optimal soft-sensing instrument and method based on an RBF neutral network optimized by a fruit fly optimization algorithm. The sea clutter optimal soft-sensing instrument comprises a radar, an on-site intelligent instrument, a control station, an on-site database for storing data, an optimal soft-sensing upper computer based on an improved RBF neutral networkoptimized by the fruit fly optimization algorithm, and a forecasting soft-sensing value display instrument. The optimal soft-sensing upper computer based on the RBF neural network optimized by the fruit fly optimization algorithm includes a data preprocessing module, an RBF neural network module and a model updating module. According to the invention, the on-line optimal soft-sensing of the sea clutter is realized, the random effect caused by human factors is overcome, the stability of the model forecasting is improved, and the probability that the model forecasting falls into a local optimumis reduced.

Description

technical field [0001] The invention relates to the field of optimal soft measuring instruments and methods, in particular to a sea clutter optimal soft measuring instrument and method based on fruit fly optimization algorithm to optimize RBF fuzzy neural network. Background technique [0002] In the radar field, the echo signal reflected from the seawater surface is called sea clutter, which is related to various factors such as sea conditions, wind tides, and radar parameters. For coastal warning radars, shipboard radars and other radars working in the marine environment, serious sea surface reflection echoes will affect the detection and tracking performance of sea surface targets. It is important to grasp the nature of sea clutter and establish an accurate sea clutter model. A prerequisite for analyzing and improving radar performance. The statistical properties of sea clutter include amplitude properties and correlation properties. Correlation properties of sea clutte...

Claims

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Application Information

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IPC IPC(8): G01S7/36G06N3/00G06N3/08
CPCG01S7/36G06N3/006G06N3/08
Inventor 刘兴高王文川王志诚朱宇张泽银余渝生宋政吉张天键
Owner ZHEJIANG UNIV
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