Active noise control method and system based on fuzzy neural network and helicopter driver helmet

A technology of active noise control and fuzzy neural network, which is applied in active noise control, sounding equipment, instruments, etc., can solve problems such as application limitations, and achieve the effect of reducing the amount of calculation, speeding up noise reduction, improving convergence speed and learning accuracy

Pending Publication Date: 2017-10-13
邢优胜
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Active noise reduction schemes can effectively reduce low-frequency noise, while traditional active noise control schemes generally can only control noise below 200Hz, and can only control noise for narrow bands, so their applications are greatly limited

Method used

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  • Active noise control method and system based on fuzzy neural network and helicopter driver helmet
  • Active noise control method and system based on fuzzy neural network and helicopter driver helmet
  • Active noise control method and system based on fuzzy neural network and helicopter driver helmet

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Experimental program
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Embodiment 1

[0029] This embodiment provides an active noise control method based on a fuzzy neural network. see figure 1 Shown is the specific embodiment of the active noise control method based on the fuzzy neural network in this application, and the steps in this embodiment include:

[0030] Step 101: collect the reference noise signal of the reference area by the reference microphone, use it as a reference signal, and output it to the fuzzy controller;

[0031] Step 102: the error microphone collects the residual noise signal in the noise control area as an error signal, and outputs it to the fuzzy controller;

[0032] Step 103: The fuzzy controller analyzes the reference signal and the error signal based on an adaptive filtering RBF (Filter-x RadialBasis Function, hereinafter referred to as FX-RBF) of the fuzzy neural network, and outputs an inverted target sound signal to speaker.

[0033] Wherein, the fuzzy controllers are two independent fuzzy controllers, which respectively ana...

Embodiment 2

[0048] In order to make the description of the present invention clearer and more detailed, and to facilitate the understanding of technical personnel, this embodiment provides an active noise control system based on a fuzzy neural network, see figure 2 Shown is a specific schematic diagram of the active noise control system based on the fuzzy neural network of the present application.

[0049] An active noise control system based on a fuzzy neural network, comprising: a fuzzy controller, a reference microphone, an error microphone and a loudspeaker, wherein:

[0050] The reference microphone is used to collect a reference noise signal in a reference area as a reference signal, and output it to a fuzzy controller;

[0051] The error microphone is used to collect the residual noise signal in the noise control area as an error signal and output it to the fuzzy controller; and

[0052] The fuzzy controller is coupled with the reference microphone and the error microphone, analy...

Embodiment 3

[0055] In order to make the description of the present invention clearer and more detailed, and to facilitate the understanding of technical personnel, this embodiment provides a helicopter pilot helmet using the active noise control system of the fuzzy neural network described in Embodiment 2. For details, see image 3 As shown in the schematic diagram of the helicopter driver's helmet, the active noise control system in Embodiment 2 also includes the main body of the driver's helmet, and a plurality of miniature microphones are installed on the surface of the helmet to collect the noise in the noise reference area as a reference signal, as shown in the figure ref.L and ref.R are the reference microphones (such as reference microphones) of the left and right earmuffs respectively; an error microphone (such as an error microphone) is installed in the residual noise control area formed by the earmuffs and the human ear to collect residual noise, As error signals, err.L and err.R...

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PUM

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Abstract

The invention discloses an active noise control method and system based on a fuzzy neural network and a helicopter pilot's helmet. The method includes collecting a reference noise signal in a reference area by a reference microphone as a reference signal, and outputting it to a fuzzy controller; collecting noise by an error microphone The residual noise signal in the control area is used as an error signal, and is output to a fuzzy controller; and the fuzzy controller is based on an adaptive FX-RBF network training algorithm of a fuzzy neural network, and analyzes the reference signal and the error signal , and output the anti-phase target sound signal to the speaker. The active noise control based on the fuzzy neural network of the present invention has obvious noise reduction effect on noise below 2000 Hz, and the noise reduction effect on low frequency noise below 1000 Hz is particularly significant.

Description

technical field [0001] The invention relates to the noise control technology in the aerospace field, in particular to an active noise control method and system based on a fuzzy neural network and a helicopter pilot's helmet. Background technique [0002] With the improvement of helicopter engine power and the increase of onboard auxiliary equipment, the noise in the helicopter cockpit is also increasing. Pilots who have been exposed to strong noise for a long time will have different degrees of hearing loss. Among them, low-frequency noise is extremely harmful to the pilot's nervous and cardiovascular systems. Conventional helicopter pilot helmets are mostly filled with sound-absorbing materials to control high-frequency noise, and the control effect on low-frequency noise at 2000 Hz is poor. The noise of domestic small transport planes and helicopters is mostly low-to-medium frequency, and the sound frequency range of on-board audio devices and communication systems also b...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10K11/178
CPCG10K11/178G10K2210/1281G10K2210/3029G10K2210/3038
Inventor 邢优胜
Owner 邢优胜
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