CN107230472A - Noise initiative control method and system in a kind of helicopter cockpit - Google Patents
Noise initiative control method and system in a kind of helicopter cockpit Download PDFInfo
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- CN107230472A CN107230472A CN201710523845.2A CN201710523845A CN107230472A CN 107230472 A CN107230472 A CN 107230472A CN 201710523845 A CN201710523845 A CN 201710523845A CN 107230472 A CN107230472 A CN 107230472A
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
- G10K2210/10—Applications
- G10K2210/128—Vehicles
- G10K2210/1281—Aircraft, e.g. spacecraft, airplane or helicopter
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
- G10K2210/30—Means
- G10K2210/301—Computational
- G10K2210/3038—Neural networks
Abstract
The invention discloses noise initiative control method and system in a kind of helicopter cockpit, method includes referring to the multiple main noise source noises of microphone pick, is inputted as reference signal；Tach signal under speed probe collection different rotating speeds, provides reference signal for rotor noise control, is inputted as reference signal；Residual noise after the control of error microphone acquisition noise, is inputted as error signal；Fuzzy controller receives the adaptive FX RBF nets training algorithm of reference-input signal, error input signal based on fuzzy neural network and reference signal and error signal is analyzed, and exports anti-phase target acoustic signal y [n] to loudspeaker and main noise source noise xa[n] is superimposed.Active noise controlling of the invention based on fuzzy neural network, has obvious noise reduction to below 2000Hz noise, wherein, the noise reduction to below 1000Hz low-frequency noises is especially pronounced.
Description
Technical field
The present invention relates to the noise control technique of aerospace field, noise master in more particularly to a kind of helicopter cockpit
Flowing control method and system.
Background technology
With the raising of helicopter engine power, the increase of airborne auxiliary equipment, the noise in helicopter cockpit
It is increasing, it is chronically exposed to and different degrees of acoustic trauma is had compared with the aircrew under strong noise environment.Helicopter drives
It is low-frequency noise, making an uproar produced by the engagement of decelerator internal gear that noise in cabin, which mainly includes rotor and the noise of tail-rotor generation,
Sound is medium-high frequency structure-borne sound, and the sound frequency range of airborne audio devices and communication system falls within middle low frequency, and wideband background is made an uproar
Sound etc., wherein, it is very harmful that low-frequency noise is caused to pilot's nerve, cardiovascular system.
Traditional passive control technology is due to the limitation of size, weight, and mainly the control effect to high-frequency noise is preferable, right
The control effect of 2000Hz Middle and low frequency noises is poor, can not adapt to the change of lifting airscrew rotating speed.Using active noise reduction side
Case can effectively reduce low-frequency noise, and traditional active noise control program can only typically control below 200Hz noise, right
The raising of ability to communicate has little effect in the protection of Pilot Hearing, high-altitude flight.
For the ability to communicate protected the hearing of pilot, improved in high-altitude flight, it is badly in need of releasing a helicopter driving
Interior noise Active Control Method and system, realize the Noise measarement in larger frequency bandwidth.
The content of the invention
In view of the problem of above-mentioned prior art is present, the present invention provides noise impedance side in a kind of helicopter cockpit
Method and system.
The present invention solves technical problem and adopted the following technical scheme that：Noise impedance side in a kind of helicopter cockpit
Method, including：With reference to the multiple main noise source noise x of microphone picka[n], is inputted as reference signal；Speed probe is gathered not
With the tach signal under rotating speed, reference signal x is provided for rotor noise controlb[n], is inputted as reference signal；Error is transaudient
Residual noise e [n] after the control of device acquisition noise, is inputted as error signal；Fuzzy controller receives reference-input signal x
[n]=xa[n]+xb[n], error input signal e [n], the adaptive FX-RBF nets training algorithm based on fuzzy neural network is to ginseng
Examine signal and error signal is analyzed, and export anti-phase target acoustic signal y [n] to loudspeaker；And loudspeaker sends use
In the target acoustic signal y [n] for offsetting main noise source noise, with main noise source noise xa[n] is superimposed.
Optionally, the fuzzy controller includes acoustic mode extractor, using the acoustic mode extractor for needing
The amplitude of the main noise source to be controlled, energy, phase, frequency, the acoustic properties of direction and statistical property, from it is a series of not
At least one is extracted in related reference acoustic mode and refers to acoustic mode.
Optionally, the fuzzy controller is based on multiple uncorrelated reference acoustic modes above and carries out fuzzy controls, and defeated
Go out noise control mode.
Optionally, the noise control mode include but is not limited to noise source quantity, it is the position of the main noise source, described
The acoustic properties of the type of main noise source, the main noise source.
Optionally, the adaptive FX-RBF nets training algorithm includes：Reference noise is believed based on FuzzycMeans Clustering
Number collection is divided；The material calculation of each iteration is adaptively reduced based on approximate gradient descent method, and is dynamically adjusted most
The learning rate of small mean square deviation algorithm.
Optionally, the adaptive FX-RBF nets training algorithm also includes：Amplitude and phase to time lag signal are divided
Exported after analysis, reconstruct inverting target acoustical signal to loudspeaker.
Optionally, it is described to include the step of divided based on FuzzycMeans Clustering to reference noise signal data set：
Using Gaussian function as the basic function of algorithm, it is determined that being adapted to the optimal cluster number c and optimal weight of actual noise situation
After factor m, the center vector of hidden layer is learnt, until center vector no longer changes or less than predetermined threshold value, and calculated
Gaussian function variance；The material calculation that each iteration is adaptively reduced based on approximate gradient descent method, and dynamically adjust most
The step of learning rate of small mean square deviation algorithm, includes：Based on approximate gradient descent method, formula α (t+1)=α (t)-β is utilized
Δ E adaptively reduces the material calculation of each iteration, and dynamically adjusts the learning rate of lms algorithm；Then initialize
Hidden layer recycles formula to each weights of output layerWeights are counted
Calculate, until output error [y0(n)-y (n)] no longer change or during less than some default threshold value, stop study, otherwise continue weight
Multiple above learning procedure；α is learning rate in formula, and Δ E is the error before and after training, and β is adaptive adjusting step, and w is implicit
Layer arrives the weights of output layer, y0(n) it is target output value, y (n) is real output value.
Optionally, the reference microphone is located at the top of crew department and at operating desk；The speed probe is located at hair
In motivation；The error microphone is located at flight deck seat at human ear；The loudspeaker is located at the top of crew department and seat
At the top of chair；The fuzzy controller is located at below pilot set.
The present invention solves technical problem and also adopted the following technical scheme that：Noise impedance in a kind of helicopter cockpit
System, including：With reference to microphone, coupled with fuzzy controller, for gathering multiple main noise source noise xa[n], is used as reference
Signal is inputted；Speed probe, couples with fuzzy controller, is rotor noise for gathering the tach signal under different rotating speeds
Control provides reference signal xb[n], is inputted as reference signal；Error microphone, is coupled with fuzzy controller, is made an uproar for gathering
Residual noise e [n] after acoustic control, is inputted as error signal；Fuzzy controller, with the reference microphone, revolution speed sensing
Device, error microphone and loudspeaker coupling, for receiving reference-input signal x [n]=xa[n]+xb[n], error input signal e
[n], the adaptive FX-RBF nets training algorithm based on fuzzy neural network is analyzed reference signal and error signal, and defeated
Go out anti-phase target acoustic signal y [n] to loudspeaker；And loudspeaker, coupled with the fuzzy controller, be used to support for sending
Disappear the target acoustic signal y [n] of main noise source noise, with main noise source noise xa[n] is superimposed.
Optionally, the fuzzy controller includes acoustic mode extractor, using the acoustic mode extractor for needing
The amplitude of the main noise source to be controlled, energy, phase, frequency, the acoustic properties of direction and statistical property, from it is a series of not
At least one is extracted in related reference acoustic mode and refers to acoustic mode；The fuzzy controller is based on multiple uncorrelated above
Fuzzy control, and output noise control model are carried out with reference to acoustic mode；The noise control mode includes but is not limited to noise
Source quantity, the position of the main noise source, the type of the main noise source, the acoustic properties of the main noise source；The reference
Microphone is located at the top of crew department and at operating desk；The speed probe is located on engine；The error microphone position
In flight deck seat at human ear；The loudspeaker is located at the top of crew department and seat tops；The fuzzy controller position
Below pilot set.
The present invention has the advantages that：
1. inputted using multiple reference signals of the function applied to polymorphic type sound source will be extracted with reference to acoustic mode extractor
In, by Adaptive adjusting algorithm extract the optimization of function, can effectively ensure the degree of accuracy of extracted acoustic mode；
2. Active noise control system in helicopter cockpit, there is obvious noise reduction to imitate below 2000Hz noise
Really, wherein, the noise reduction to below 1000Hz low-frequency noises is especially pronounced；
3. carrying out automatically adjusting for learning rate based on adaptive FX-RBF nets training algorithm, the same of the stability of a system is being ensured
When, improve algorithm the convergence speed and study precision；
4. carrying out the reconstruct of reverse target acoustic signal based on adaptive FX-RBF algorithms, anti-noise sound source Time Delay is being solved
While, effectively increase noise reduction.
Brief description of the drawings
Fig. 1 is the embodiment schematic diagram of noise initiative control method in a kind of helicopter cockpit of the present invention；
Fig. 2 is schematic diagram in a kind of helicopter cockpit of the present invention；
Fig. 3 is the fundamental diagram of acoustic mode extractor provided in an embodiment of the present invention；
Fig. 4 is fuzzy controller illustraton of model provided in an embodiment of the present invention；
Fig. 5 show the specific schematic diagram of Active noise control system in the application helicopter cockpit.
Embodiment
Technical scheme is further elaborated with reference to embodiment and accompanying drawing.
Some vocabulary have such as been used to censure specific components among specification and claim.Those skilled in the art should
It is understood that hardware manufacturer may call same component with different nouns.This specification and claims are not with name
The difference of title is used as the mode for distinguishing component, but is used as the criterion of differentiation with the difference of component functionally.Such as logical
The "comprising" of piece specification and claim mentioned in is an open language, therefore should be construed to " include but do not limit
In "." substantially " refer in receivable error range, those skilled in the art can solve described in the range of certain error
Technical problem, basically reaches the technique effect.In addition, " coupling " one word is herein comprising any direct and indirect electric property coupling
Means.Therefore, if a first device is coupled to a second device described in text, representing the first device can directly electrical coupling
The second device is connected to, or the second device is electrically coupled to indirectly by other devices or coupling means.Specification
Subsequent descriptions for implement the application better embodiment, so it is described description be for the purpose of the rule for illustrating the application,
It is not limited to scope of the present application.The protection domain of the application is worked as to be defined depending on the appended claims person of defining.
Embodiment 1
Present embodiments provide noise initiative control method in a kind of helicopter cockpit.Shown in Figure 1 is the application
Step includes in the specific embodiment of noise initiative control method, the present embodiment in middle helicopter cockpit：
Step S1-1：With reference to the multiple main noise source noise x of microphone picka[n], is inputted as reference signal；
Step S1-2：Tach signal under speed probe collection different rotating speeds, is provided with reference to letter for rotor noise control
Number xb[n], is inputted as reference signal；
Step S1-3：Residual noise e [n] after the control of error microphone acquisition noise, is inputted as error signal；Step
S2：Fuzzy controller receives reference-input signal x [n]=xa[n]+xb[n], error input signal e [n], based on fuzznet
Adaptive-filtering RBF (Filter-x Radial Basis Function, hereinafter referred to as FX-RBF) net training algorithm pair of network
Reference signal and error signal are analyzed, and export anti-phase target acoustic signal y [n] to loudspeaker；And
Step S3：Loudspeaker sends the target acoustic signal y [n] for offsetting main noise source noise, with main noise source noise
xa[n] is superimposed.
Wherein, it is located at reference to microphone at the top of crew department and at operating desk；Speed probe is located on engine；Error
Microphone is located at flight deck seat at human ear；Loudspeaker is located at the top of crew department and seat tops；Fuzzy controller position
Below pilot set, as shown in Figure 2.
Wherein, fuzzy controller includes acoustic mode extractor, as shown in figure 3, Fig. 3 is sound provided in an embodiment of the present invention
Learn the fundamental diagram of pattern extractor.
Using acoustic mode extractor for the amplitude of main noise source for needing to control, energy, phase, frequency, direction and
The acoustic properties of statistical property, extraction at least one refers to acoustic mode from a series of incoherent reference acoustic modes.Mould
Fuzzy controllers are based on multiple uncorrelated reference acoustic modes above and carry out fuzzy controls, and output noise control model, noise control
Molding formula includes but is not limited to noise source quantity, the position of main noise source, the type of main noise source, the acoustics category of main noise source
Property.
Set in fuzzy controller and refer to acoustic mode extractor, function will be extracted using with reference to acoustic mode extractorApplied to described multiple reference signals input x [n], then to passing through extraction algorithmIt is defeated after processing
Go out with reference to acoustic mode s [n]；Then estimation function α is applied to output reference model s using with reference to acoustic mode extractor
[n], after to compared estimate α (s [n]) extract the adaptive adjustment of function.
Fig. 4 is fuzzy controller illustraton of model provided in an embodiment of the present invention.Made an uproar in the helicopter cockpit that the present invention is provided
Sound Active Control Method mainly includes：Based on sound system unintentional nonlinearity characteristic, neutral net is introduced into fuzzy control, constituted
Fuzzy neural network, according to input and output sample, by using the learning method of neutral net, Automated Design and the fuzzy system of adjustment
The self study of system and adaptation function, improve the degree of accuracy and the noise reduction of active noise controlling；Wherein x (n) is system reference
Noise signal, e (n) is residual noise signal, and y (n) is controller output signal.
As illustrated, the type of processing unit is divided into three classes in neutral net：Input layer, output layer, hidden layer；
Input layer：Noise source signal and data outside receiving；Input layer signal in the present invention is " addition delay link
Reference signal input vector "；
Output layer：Realize the output of system result；In the present invention, be " by optimal noise control mode export to
Loudspeaker, drives it to send corresponding target acoustic signal "；
Hidden layer：Line translation is entered to input vector, between input and output layer, the processing do not observed by its exterior
Unit；In the present invention, it is " the reference signal input vector of low-dimensional to be converted into higher dimensional space, realizes that the linear of sound system is asked
Inscribe the linear separability in higher dimensional space ".
Adaptive FX-RBF net training algorithms include：Reference noise signal data set is carried out based on FuzzycMeans Clustering
Divide；The material calculation of each iteration is adaptively reduced based on approximate gradient descent method, and dynamically adjusts lms algorithm
Learning rate.
It is above-mentioned to include the step of divided based on FuzzycMeans Clustering to reference noise signal data set：
Using Gaussian function as the basic function of algorithm, it is determined that be adapted to actual noise situation optimal cluster number c and
After optimal weight factor m, the center vector of hidden layer is learnt, until center vector no longer changes or less than default threshold
Value, and calculate Gaussian function variance.
The above-mentioned material calculation that each iteration is adaptively reduced based on approximate gradient descent method, and dynamically adjust lowest mean square
The step of learning rate of difference algorithm, includes：
Based on approximate gradient descent method, the meter of each iteration is adaptively reduced using formula α (t+1)=α (t)-β Δs E
Step-length is calculated, and dynamically adjusts the learning rate of lms algorithm, more efficient accelerates convergence of algorithm speed；
Then initialization hidden layer recycles formula to each weights of output layerIt is right
Weights are calculated, until output error [y0(n)-y (n)] no longer change or during less than some default threshold value, stop study,
Otherwise continue to repeat above learning procedure；The receipts of algorithm are improved by dynamic adaptive regularized learning algorithm speed in learning process
Speed is held back, so as to improve study precision.
α is learning rate in formula, and Δ E is the error before and after training, and β is adaptive adjusting step, and w is hidden layer to output
The weights of layer, y0(n) it is target output value, y (n) is real output value.
Wherein, the adaptive FX-RBF nets training algorithm also includes：Amplitude and phase to time lag signal are analyzed,
Exported after reconstruct inverting target acoustical signal to loudspeaker, the time lag signal refers to that reference signal x (t) waits to eliminate the noise propagating to
In the path of point, the signal y (t) produced due to time delay；The relation of time lag signal and reference signal：Y (t)=x (t)-x
(t-t0), wherein, x (t) is reference signal, and y (t) is time lag signal, t0For lag time.
Embodiment 2
In order that present invention description is more clearly and detailed, while being easy to technical staff to understand, the present embodiment provides a kind of straight
Active noise control system in machine driving cabin is risen, it is shown in Figure 5 for noise impedance system in the application helicopter cockpit
The specific schematic diagram of system.
Active noise control system in a kind of helicopter cockpit, including：
With reference to microphone, coupled with fuzzy controller, for gathering multiple main noise source noise xa[n], as with reference to letter
Number input；
Speed probe, couples with fuzzy controller, is rotor noise control for gathering the tach signal under different rotating speeds
System provides reference signal xb[n], is inputted as reference signal；
Error microphone, couples with fuzzy controller, for the residual noise e [n] after acquisition noise control, is used as error
Signal is inputted；
Fuzzy controller, with being coupled with reference to microphone, speed probe, error microphone and loudspeaker, for receiving ginseng
Examine input signal x [n]=xa[n]+xb[n], error input signal e [n], the adaptive FX-RBF nets based on fuzzy neural network
Training algorithm is analyzed reference signal and error signal, and exports anti-phase target acoustic signal y [n] to loudspeaker；And
Loudspeaker, is coupled with fuzzy controller, for sending the target acoustic signal y [n] for offsetting main noise source noise,
With main noise source noise xa[n] is superimposed.
Wherein, above-mentioned reference microphone and error microphone are microphone.
Fuzzy controller includes acoustic mode extractor, using acoustic mode extractor for needing the main noise source of control
Amplitude, energy, phase, frequency, the acoustic properties of direction and statistical property, from a series of incoherent reference acoustic modes
Extract at least one and refer to acoustic mode.
Fuzzy controller is based on multiple uncorrelated reference acoustic modes above and carries out fuzzy controls, and output noise control mould
Formula.Noise control mode includes but is not limited to noise source quantity, the position of main noise source, the type of main noise source, main noise source
Acoustic properties.
Wherein, it is located at reference to microphone at the top of crew department and at operating desk；Speed probe is located on engine；Error
Microphone is located at flight deck seat at human ear；Loudspeaker is located at the top of crew department and seat tops；Fuzzy controller position
Below pilot set.
In summary, noise initiative control method and system in a kind of helicopter cockpit for providing of the present invention, it is and existing
Technology is compared, and is had the advantages that：
1. Active noise control system in helicopter cockpit, there is obvious noise reduction to imitate below 2000Hz noise
Really, wherein, the noise reduction to below 1000Hz low-frequency noises is especially pronounced；Made an uproar in helicopter cockpit provided by the present invention
Sound active control system, can realize the active control of senior middle school's low-frequency noise in helicopter cockpit；
2. inputted using multiple reference signals of the function applied to polymorphic type sound source will be extracted with reference to acoustic mode extractor
In, by Adaptive adjusting algorithm extract the optimization of function, can effectively ensure the degree of accuracy of extracted acoustic mode；
3. carrying out automatically adjusting for learning rate based on adaptive FX-RBF nets training algorithm, the same of the stability of a system is being ensured
When, improve algorithm the convergence speed and study precision；
4. directly being controlled time lag noise based on adaptive FX-RBF algorithms, efficiently solve anti-noise sound source time lag and ask
Topic, and improve noise reduction.
For ease of description, the quality of embodiment is not only represented for the sequencing of above example.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used
To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and
Scope.
Claims (10)
1. noise initiative control method in a kind of helicopter cockpit, it is characterised in that including：
With reference to the multiple main noise source noise x of microphone picka[n], is inputted as reference signal；
Tach signal under speed probe collection different rotating speeds, reference signal x is provided for rotor noise controlb[n], is used as ginseng
Examine signal input；
Residual noise e [n] after the control of error microphone acquisition noise, is inputted as error signal；
Fuzzy controller receives reference-input signal x [n]=xa[n]+xb[n], error input signal e [n], based on fuzzy neural
The adaptive FX-RBF nets training algorithm of network is analyzed reference signal and error signal, and exports anti-phase target sound letter
Number y [n] is to loudspeaker；And
Loudspeaker sends the target acoustic signal y [n] for offsetting main noise source noise, with main noise source noise xa[n] is superimposed.
2. noise initiative control method in helicopter cockpit according to claim 1, it is characterised in that the Fuzzy Control
Device processed includes acoustic mode extractor, utilizes the acoustic mode extractor shaking for the main noise source of needs control
Width, energy, phase, frequency, the acoustic properties of direction and statistical property, are extracted from a series of incoherent reference acoustic modes
At least one refers to acoustic mode.
3. noise initiative control method in helicopter cockpit according to claim 2, it is characterised in that the Fuzzy Control
Device processed is based on multiple uncorrelated reference acoustic modes above and carries out fuzzy controls, and output noise control model.
4. noise initiative control method in helicopter cockpit according to claim 3, it is characterised in that the noise control
Molding formula includes but is not limited to noise source quantity, the position of the main noise source, the type of the main noise source, the main noise
The acoustic properties in source.
5. noise initiative control method in helicopter cockpit according to claim 1, it is characterised in that described adaptive
FX-RBF net training algorithms include：Reference noise signal data set is divided based on FuzzycMeans Clustering；Based on approximate
Gradient descent method adaptively reduces the material calculation of each iteration, and dynamically adjusts the learning rate of lms algorithm.
6. noise initiative control method in helicopter cockpit according to claim 1, it is characterised in that described adaptive
FX-RBF net training algorithms also include：Amplitude and phase to time lag signal are analyzed, defeated after reconstruct inverting target acoustical signal
Go out to loudspeaker.
7. noise initiative control method in helicopter cockpit according to claim 5, it is characterised in that described to be based on mould
The step of paste c- mean clusters are divided to reference noise signal data set includes：
Using Gaussian function as the basic function of algorithm, it is determined that being adapted to the optimal cluster number c of actual noise situation and optimal
After weight factor m, the center vector of hidden layer is learnt, until center vector no longer changes or less than predetermined threshold value, and
Calculate Gaussian function variance；
The material calculation that each iteration is adaptively reduced based on approximate gradient descent method, and dynamically adjustment Minimum Mean Square Error is calculated
The step of learning rate of method, includes：
Based on approximate gradient descent method, walked using formula α (t+1)=α (the t)-β Δs E calculating for adaptively reducing each iteration
It is long, and dynamically adjust the learning rate of lms algorithm；
Then initialization hidden layer recycles formula to each weights of output layerIt is right
Weights are calculated, until output error [y0(n)-y (n)] no longer change or during less than some default threshold value, stop study,
Otherwise continue to repeat above learning procedure；
α is learning rate in formula, and Δ E is the error before and after training, and β is adaptive adjusting step, and w is that hidden layer arrives output layer
Weights, y0(n) it is target output value, y (n) is real output value.
8. noise initiative control method in the helicopter cockpit according to claim any one of 1-7, it is characterised in that institute
State and be located at reference to microphone at the top of crew department and at operating desk；The speed probe is located on engine；The error is passed
Sound device is located at flight deck seat at human ear；The loudspeaker is located at the top of crew department and seat tops；The Fuzzy Control
Device processed is located at below pilot set.
9. Active noise control system in a kind of helicopter cockpit, it is characterised in that including：
With reference to microphone, coupled with fuzzy controller, for gathering multiple main noise source noise xa[n], it is defeated as reference signal
Enter；
Speed probe, is coupled with fuzzy controller, for gathering the tach signal under different rotating speeds, is that rotor noise control is carried
Signal x for referenceb[n], is inputted as reference signal；
Error microphone, couples with fuzzy controller, for the residual noise e [n] after acquisition noise control, is used as error signal
Input；
Fuzzy controller, is coupled with the reference microphone, speed probe, error microphone and loudspeaker, for receiving ginseng
Examine input signal x [n]=xa[n]+xb[n], error input signal e [n], the adaptive FX-RBF nets based on fuzzy neural network
Training algorithm is analyzed reference signal and error signal, and exports anti-phase target acoustic signal y [n] to loudspeaker；And
Loudspeaker, is coupled with the fuzzy controller, for sending the target acoustic signal y [n] for offsetting main noise source noise,
With main noise source noise xa[n] is superimposed.
10. Active noise control system in helicopter cockpit according to claim 9, it is characterised in that described fuzzy
Controller includes acoustic mode extractor, utilizes the acoustic mode extractor shaking for the main noise source of needs control
Width, energy, phase, frequency, the acoustic properties of direction and statistical property, are extracted from a series of incoherent reference acoustic modes
At least one refers to acoustic mode；The fuzzy controller is based on multiple uncorrelated reference acoustic modes above and carries out Fuzzy Controls
System, and output noise control model；The noise control mode includes but is not limited to noise source quantity, the position of the main noise source
Put, the type of the main noise source, the acoustic properties of the main noise source；The reference microphone be located at crew department at the top of and
At operating desk；The speed probe is located on engine；The error microphone is located at flight deck seat at human ear；Institute
Loudspeaker is stated positioned at crew department top and seat tops；The fuzzy controller is located at below pilot set.
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| CN107248408A (en) * | 2017-06-30 | 2017-10-13 | 邢优胜 | A kind of active noise controlling method based on fuzzy neural network, system and helicopter pilot's helmet |
| CN108182934A (en) * | 2018-01-19 | 2018-06-19 | 泊鹭（荆门）飞机有限公司 | A kind of baby plane Cockpit Noise suppression system |
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| CN109714689A (en) * | 2018-12-21 | 2019-05-03 | 南京理工大学 | A kind of directionality acoustics index acquisition methods based on difference microphone linear array |
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| CN109625261A (en) * | 2017-10-06 | 2019-04-16 | 松下电器（美国）知识产权公司 | Unmanned vehicle |
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| CN113668765A (en) * | 2021-08-16 | 2021-11-19 | 广西大学 | Intelligent explosion-proof noise-reducing wall |
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| JP7465039B2 (en) | 2021-12-06 | 2024-04-10 | 南京航空航天大学 | Active noise control system for helicopters incorporating acoustic array and propeller control. |
| CN114255733A (en) * | 2021-12-21 | 2022-03-29 | 中国空气动力研究与发展中心低速空气动力研究所 | Self-noise masking system and flight equipment |
| CN114255733B (en) * | 2021-12-21 | 2023-05-23 | 中国空气动力研究与发展中心低速空气动力研究所 | Self-noise masking system and flight device |
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