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A self-organizing TS fuzzy network modeling method for infrared flame recognition

A flame recognition and fuzzy network technology, applied in the field of infrared flame recognition

Active Publication Date: 2019-01-25
无锡格林通安全装备有限公司
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

Finally, an adaptive learning algorithm is designed to overcome the local optimum problem in gradient descent learning

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  • A self-organizing TS fuzzy network modeling method for infrared flame recognition
  • A self-organizing TS fuzzy network modeling method for infrared flame recognition
  • A self-organizing TS fuzzy network modeling method for infrared flame recognition

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Embodiment Construction

[0114] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the accompanying drawings in the embodiments of the application. Apparently, the described embodiments are only part of the embodiments of the application, not all of them. Based on the implementation manners in this application, all other implementation manners obtained by persons of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0115]A self-organized TS type fuzzy network modeling method applied to infrared flame identification of the present application, the steps are as follows:

[0116] (1) Collect the time-domain signal data of different flames, and preprocess the signal data to obtain the frequency-domain signal data,

[0117] The preprocessing steps are:

[0118] (1.1) Subtract the reference voltage from the collected time-domain signal, and add a Hanning window to the ...

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Abstract

The invention discloses a self-organizing TS-type fuzzy network modeling method applied to infrared flame recognition, comprising the following steps: (1) collecting time-domain signal data of different flames and interference sources, and preprocessing the time-domain signal data to obtain frequency-domain signal data; (2) extracting the characteristic information from the time-domain and frequency-domain signal data of the waveform, obtaining the characteristic vector of the flame, and composing a sample set; (3) dividing the sample set into training set, verification set and test set; (4) building TS-RBF fuzzy neural network; (5) setting TS-RBF fuzzy neural network parameters initial value, using the training set of samples to TS-RBF fuzzy neural network training, structure, parameter learning; (6) using verification sets to test the training TS- Verification and Model Selection of RBF Fuzzy Neural Network; (7) inputting the test set into the trained TS-RBF fuzzy neural network, theresults as the final evaluation of the model.

Description

technical field [0001] The invention belongs to the technical field of infrared flame identification, and in particular relates to a self-organized TS type fuzzy network modeling method applied to infrared flame identification. Background technique [0002] Flame detectors based on infrared pyroelectric sensors are widely used in the flame detection of modern industrial hydrocarbons, and are an important part of the automatic operation of industrial production systems and a necessary safety device. The wavelength of infrared light radiated by hydrocarbon flames absorbed by carbon dioxide is relatively fixed in the spectrum, but the corresponding sampling signal may be affected by other interference sources, and the signals of these interference sources can be detected in other bands of the spectrum. Generally speaking, sensors in different bands in flame detectors have different sensitivities to fire sources and interference sources, so flames and interference sources can be...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G01J5/00
CPCG06N3/08G01J5/0018G06N3/043G06F18/217G06F18/214
Inventor 谢林柏温子腾谭勇冯宏伟
Owner 无锡格林通安全装备有限公司
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