CN107248408A - A kind of active noise controlling method based on fuzzy neural network, system and helicopter pilot's helmet - Google Patents
A kind of active noise controlling method based on fuzzy neural network, system and helicopter pilot's helmet Download PDFInfo
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- 238000004458 analytical method Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000011478 gradient descent method Methods 0.000 claims description 9
- 240000006409 Acacia auriculiformis Species 0.000 claims description 7
- 210000005069 ears Anatomy 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 5
- 210000003128 head Anatomy 0.000 description 5
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- 229920000049 Carbon (fiber) Polymers 0.000 description 1
- 206010011903 Deafness traumatic Diseases 0.000 description 1
- 208000002946 Noise-Induced Hearing Loss Diseases 0.000 description 1
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- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 1
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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
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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
- 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
- G10K2210/3029—Fuzzy logic; Genetic algorithms
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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
- 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
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- Acoustics & Sound (AREA)
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- Soundproofing, Sound Blocking, And Sound Damping (AREA)
Abstract
The invention discloses a kind of active noise controlling method based on fuzzy neural network, system and helicopter pilot's helmet, method includes the reference noise signal that reference microphone gathers reference zone, as reference signal, and exports to fuzzy controller；The residual noise signal of error microphone acquisition noise control area, as error signal, and is exported to fuzzy controller；And adaptive FX RBF net training algorithm of the fuzzy controller based on fuzzy neural network, the reference signal and the error signal are analyzed, and anti-phase target acoustic signal is exported to loudspeaker, active noise controlling of the invention based on fuzzy neural network, there is 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, more particularly to a kind of master based on fuzzy neural network
Moving noise control method, system and helicopter pilot's helmet.
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, wherein, low frequency is made an uproar
It is very harmful that sound is caused to pilot's nerve, cardiovascular system.Conventional helicopter pilot's helmet is more to be absorbed sound by filling
The mode of material carries out the control of high-frequency noise, and the control effect to 2000Hz Middle and low frequency noises is poor.Domestic small-sized transport
The noise of machine and helicopter is more based on middle low frequency, and the sound frequency range of airborne audio devices and communication system falls within middle low frequency.
Low-frequency noise can effectively be reduced using active noise reduction scheme, and traditional active noise control program typically can only
Below 200Hz noise is controlled, and Noise measarement can be only carried out for arrowband, therefore its application is very restricted.
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
The high-efficient noise-reducing method and the helmet of member, realizes 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 a kind of active noise control based on fuzzy neural network
Method processed, system and helicopter pilot's helmet.
The present invention solves technical problem and adopted the following technical scheme that：A kind of active noise controlling based on fuzzy neural network
Method, including：Reference microphone gathers the reference noise signal of reference zone, as reference signal, and exports to fuzzy control
Device；The residual noise signal of error microphone acquisition noise control area, as error signal, and is exported to fuzzy controller；
And adaptive FX-RBF net training algorithm of the fuzzy controller based on fuzzy neural network, to the reference signal and institute
State error signal to be analyzed, and export anti-phase target acoustic signal to loudspeaker.
Optionally, the fuzzy controller is two independent fuzzy controllers, and the reference being respectively received is believed respectively
Number and error signal signal analyzed, and anti-phase target acoustic signal is exported to loudspeaker, to offset residual noise signal.
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；
Optionally, the material calculation that each iteration is adaptively reduced based on approximate gradient descent method, and dynamically adjusting
The step of learning rate of lms algorithm, includes：Based on approximate gradient descent method, using formula α (t+1)=α (t)-
β Δs E adaptively reduces the material calculation of each iteration, and dynamically adjusts the learning rate of lms algorithm；Then just
Beginningization hidden layer recycles formula to each weights of output layerWeights are entered
Row is calculated, until output error [y0(n)-y (n)] no longer change or during less than some default threshold value, stop study, otherwise after
It is continuous 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
Hidden layer is to the weights of output layer, y0(n) it is target output value, y (n) is real output value.
The present invention solves technical problem and also provides following technical scheme：A kind of active noise control based on fuzzy neural network
System processed, including：Fuzzy controller, reference microphone, error microphone and loudspeaker, wherein：The reference microphone, is used for
The reference noise signal of reference zone is gathered, as reference signal, and is exported to fuzzy controller；The error microphone, is used
Residual noise signal in acquisition noise control area, as error signal, and is exported to fuzzy controller；And it is described fuzzy
Controller is coupled with the reference microphone and the error microphone, the adaptive FX-RBF net instructions based on fuzzy neural network
Practice algorithm, the reference signal and the error signal are analyzed, and export anti-phase target acoustic signal to loudspeaker.
Optionally, the fuzzy controller is two independent fuzzy controllers, and the reference being respectively received is believed respectively
Number and error signal signal analyzed, and anti-phase target acoustic signal is exported to loudspeaker, to offset residual noise signal.
The present invention solves technical problem and also provides following technical scheme：Also include driving based on above-mentioned active noise control system
The person's of sailing helmet body, the fuzzy controller includes left ear mold fuzzy controllers, auris dextra fuzzy controller, and left and right ear is performed respectively
Signal analysis and noise reduction mode output.
Optionally, in addition to power supply, active noise control system switch, the power supply powers to fuzzy controller, actively
Noise control system switch is located between power supply and active noise control system respectively；The reference microphone, error microphone
It is integrated in loudspeaker in left ear earmuff and auris dextra earmuff；The active noise control system switch is integrated in left ear earmuff；
The fuzzy controller and power supply are integrated in helmet dead astern；The helmet is the three chamber list cushion helmets, and driver wears the head
During helmet, closed cavity is constituted by loudspeaker drum surface, earmuff pad, driver ear, is residual noise control zone；Carried on the back by loudspeaker
Face and earmuff shell form closed cavity, are noise reference region 1；It is made up of earmuff shell, helmet body, driver head
Closed cavity, is noise reference region 2.
The present invention has the advantages that：
1. the active noise control system based on fuzzy neural network, has obvious noise reduction to below 2000Hz noise
Effect, wherein, the noise reduction to below 1000Hz low-frequency noises is especially pronounced；
2. 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；
3. 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.
4. left and right ear carries out signal analysis using independent fuzzy controller respectively and noise control mode is exported, significantly
The amount of calculation of controller is reduced, noise reduction speed can be accelerated to a certain extent.
Brief description of the drawings
Fig. 1 illustrates for a kind of embodiment of active noise controlling method based on fuzzy neural network of the present invention
Figure；
Fig. 2 is fuzzy controller illustraton of model provided in an embodiment of the present invention；
Fig. 3 illustrates for a kind of embodiment of active noise control system based on fuzzy neural network of the present invention
Figure；
Fig. 4 is helicopter pilot's helmet structure schematic diagram provided in an embodiment of the present invention；
Fig. 5 is helicopter pilot's helmet auris dextra earmuff schematic diagram provided in an embodiment of the present invention.
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 a kind of active noise controlling method based on fuzzy neural network.Shown in Figure 1 is this
Step includes in the specific embodiment of the active noise controlling method based on fuzzy neural network, the present embodiment in application：
Step 101：Reference microphone gathers the reference noise signal of reference zone, as reference signal, and exports to mould
Fuzzy controllers；
Step 102：The residual noise signal of error microphone acquisition noise control area, as error signal, and is exported
To fuzzy controller；
Step 103：Adaptive-filtering RBF (Filter-x Radial of the fuzzy controller based on fuzzy neural network
Basis Function, hereinafter referred to as FX-RBF) reference signal and the error signal are analyzed, and export anti-phase
Target acoustic signal to loudspeaker.
Wherein, the fuzzy controller is two independent fuzzy controllers, respectively the reference signal to being respectively received
Analyzed with error signal signal, and export anti-phase target acoustic signal to loudspeaker, to offset residual noise signal.
Fig. 2 is fuzzy controller illustraton of model provided in an embodiment of the present invention.The present invention provide based on fuzzy neural network
Active noise controlling method mainly include：Based on sound system unintentional nonlinearity characteristic, neutral net is introduced into fuzzy control,
Fuzzy neural network is constituted, according to input and output sample, by using the learning method of neutral net, Automated Design and adjustment mould
The self study of paste 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 calculating of each iteration is adaptively reduced using formula α (t+1)=α (t)-β Δs E
Step-length, and the learning rate of lms algorithm is dynamically adjusted, more efficient accelerates convergence of algorithm speed；
Then initialization hidden layer recycles formula to each weights of output layer
Weights are calculated, until output error [y0(n)-y (n)] no longer change or during less than some default threshold value, stop learning
Practise, otherwise continue to repeat above learning procedure；Algorithm is improved by dynamic adaptive regularized learning algorithm speed in learning process
Convergence rate, 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 base
It is shown in Figure 2 to be made an uproar for active of the application based on fuzzy neural network in the active noise control system of fuzzy neural network
The specific schematic diagram of acoustic control system.
A kind of active noise control system based on fuzzy neural network, including：Fuzzy controller, reference microphone, mistake
Poor microphone and loudspeaker, wherein：
The reference microphone, the reference noise signal for gathering reference zone as reference signal, and is exported to mould
Fuzzy controllers；
The error microphone, for the residual noise signal of acquisition noise control area, as error signal, and is exported
To fuzzy controller；And
The fuzzy controller is coupled with the reference microphone and the error microphone, based on fuzzy neural network
Adaptive FX-RBF net training algorithms, are analyzed the reference signal and the error signal, and export anti-phase target
Acoustical signal is to loudspeaker.
Wherein, the fuzzy controller is two independent fuzzy controllers, respectively the reference signal to being respectively received
Analyzed with error signal signal, and export anti-phase target acoustic signal to loudspeaker, to offset residual noise signal.
Embodiment 3
In order that present invention description is more clearly and detailed, while being easy to technical staff to understand, the present embodiment, which provides one kind, to be made
With helicopter pilot's helmet of the active noise control system of the fuzzy neural network described in embodiment 2, in detail as shown in Figure 3
Active noise control system in helicopter pilot's helmet schematic diagram, embodiment 2 also includes helmet of driver main body, in the helmet
Multiple midget microphones are installed on surface, for the noise in acquisition noise reference zone, as reference signal, in figure ref.L and
Ref.R is respectively the reference microphone (such as reference microphone) of left and right earmuff；The residual noise control formed in earmuff and human ear
Alignment error microphone (such as error microphone) in area processed, for gathering residual noise, as error signal, in figure err.L and
Err.R is respectively left and right error microphone；Loudspeaker is installed in earmuff, for by exporting target acoustic signal, with noise control
Noise signal in region processed is superimposed, and MIC.L and MIC.R are respectively left and right loudspeaker in figure；Reference signal and error are believed
Number input after handling by analysis, performs signal analysis and the drop of left and right ear to the corresponding fuzzy controller of left and right ear respectively
Make an uproar pattern, export optimal noise control mode to loudspeaker；By operating active noise reduction system to switch, active noise can control
Control system is started working, is stopped.
Wherein, helicopter pilot's helmet, in addition to：Power supply, active noise control system switch, the power supply is to fuzzy
Controller is powered, and active noise control system switch is located between power supply and active noise control system respectively.
Wherein, the reference microphone, error microphone and loudspeaker are integrated in left ear earmuff and auris dextra earmuff；It is described
Active noise control system switch is integrated in left ear earmuff；The fuzzy controller and power supply are integrated in helmet dead astern.
Wherein, the helmet be the three chamber list cushion helmets, when driver wears the helmet, by loudspeaker drum surface, earmuff pad,
Driver ear constitutes closed cavity, is residual noise control zone；Closed chamber is formed by the loudspeaker back side and earmuff shell
Body, is noise reference region 1；Closed cavity is made up of earmuff shell, helmet body, driver head, is noise reference area
Domain 2.Based on helmholtz resonance chamber principle, using the resonance damping action in three layers of resonant cavity, acoustic attenuation is effectively improved
Noise in the range of characteristic, centering higher frequency has preferable sound-insulation capability.
In addition, with reference to Fig. 5, it is helicopter pilot's helmet auris dextra earmuff schematic diagram of the invention.Using with certain bullet
Property material as the main material of earmuff shell, the higher sponge of acoustic absorptivity is filled inside earmuff, and cover with outside soft
Soft flannelette, adds the comfort level of wearer；Around the size and shape of ear formula earmuff pad, all in accordance with the feature of human ear external auditory meatus
Design, form closed space with wearer ear, effectively prevent the entrance of outside noise；Needed in view of the shock insulation of the helmet
Ask, earmuff is connected by the integrated form helmet using resilient support with helmet body, when the helmet is acted on by external force, effectively reduction is worn
The discomfort on wearer head.
Using resin material, carbon fiber, porous film material etc. as helmet body material, firm same of the helmet is being kept
When alleviate helmet weight, effectively reduce constriction of the helmet to the crown, made an uproar in addition, also having isolated in the range of external high frequency
Sound；Elastomeric material, sponge, the flannelette of earmuff use belong to sound-absorbing material, can also reduce to a certain extent in the helmet
Noise.
In summary, a kind of present invention is provided active noise controlling method based on fuzzy neural network, system and straight
Machine helmet of driver is risen, compared with prior art, is had the advantages that：
1. the active noise control system based on fuzzy neural network, has obvious noise reduction to below 2000Hz noise
Effect, wherein, the noise reduction to below 1000Hz low-frequency noises is especially pronounced；Helicopter pilot's head provided by the present invention
Helmet, can realize the active control of senior middle school's low-frequency noise in helicopter cockpit；
2. 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；
3. 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.
4. left and right ear carries out signal analysis using independent fuzzy controller respectively and noise control mode is exported, significantly
The amount of calculation of controller is reduced, noise reduction speed can be accelerated to a certain extent.
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. a kind of active noise controlling method based on fuzzy neural network, it is characterised in that including：
Reference microphone gathers the reference noise signal of reference zone, as reference signal, and exports to fuzzy controller；
The residual noise signal of error microphone acquisition noise control area, as error signal, and is exported to fuzzy controller；
And
Adaptive FX-RBF net training algorithm of the fuzzy controller based on fuzzy neural network, to the reference signal and institute
State error signal to be analyzed, and export anti-phase target acoustic signal to loudspeaker.
2. the active noise controlling method according to claim 1 based on fuzzy neural network, it is characterised in that the mould
Fuzzy controllers are two independent fuzzy controllers, and the reference signal and error signal signal that are respectively received are divided respectively
Analysis, and anti-phase target acoustic signal is exported to loudspeaker, to offset residual noise signal.
3. the active noise controlling method according to claim 1 based on fuzzy neural network, it is characterised in that it is described from
Adapting to FX-RBF net training algorithms includes：Reference noise signal data set is divided based on FuzzycMeans Clustering；It is based on
Approximate gradient descent method adaptively reduces the material calculation of each iteration, and the dynamically study speed of adjustment lms algorithm
Rate.
4. the active noise controlling method according to claim 2 based on fuzzy neural network, it is characterised in that it is described from
Adapting to FX-RBF net training algorithms also includes：Amplitude and phase to time lag signal are analyzed, and reconstruct inverting target acoustical signal
After export to loudspeaker.
5. the active noise controlling method according to claim 3 based on fuzzy neural network, it is characterised in that the base
Include the step of FuzzycMeans Clustering is divided 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 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.
6. the active noise controlling method according to claim 3 based on fuzzy neural network, it is characterised in that the base
The material calculation of each iteration, and the dynamically study speed of adjustment lms algorithm are adaptively reduced in approximate gradient descent method
The step of rate, 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 layer
Weights are calculated, until output error [y0(n)-y (n)] no longer change or during less than some default threshold value, stop learning
Practise, 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.
7. a kind of active noise control system based on fuzzy neural network, it is characterised in that including：Fuzzy controller, reference
Microphone, error microphone and loudspeaker, wherein：
The reference microphone, the reference noise signal for gathering reference zone as reference signal, and is exported to Fuzzy Control
Device processed；
The error microphone, for the residual noise signal of acquisition noise control area, as error signal, and is exported to mould
Fuzzy controllers；And
The fuzzy controller is coupled with the reference microphone and the error microphone, based on the adaptive of fuzzy neural network
FX-RBF net training algorithms are answered, the reference signal and the error signal are analyzed, and export anti-phase target sound letter
Number to loudspeaker.
8. the active noise control system according to claim 7 based on fuzzy neural network, it is characterised in that the mould
Fuzzy controllers are two independent fuzzy controllers, and the reference signal and error signal signal that are respectively received are divided respectively
Analysis, and anti-phase target acoustic signal is exported to loudspeaker, to offset residual noise signal.
9. a kind of helicopter pilot's helmet of the active noise control system of the fuzzy neural network of usage right requirement 7, its
It is characterised by, the active noise control system also includes helmet of driver main body, the fuzzy controller is pasted including left ear mold
Controller, auris dextra fuzzy controller, perform the signal analysis and noise reduction mode output of left and right ear respectively.
10. helicopter pilot's helmet according to claim 9, it is characterised in that including：Power supply, active noise controlling
System switching, the power supply is powered to fuzzy controller, and active noise control system switch is located at power supply and active noise respectively
Between control system；The reference microphone, error microphone and loudspeaker are integrated in left ear earmuff and auris dextra earmuff；It is described
Active noise control system switch is integrated in left ear earmuff；The fuzzy controller and power supply are integrated in helmet dead astern；Institute
The helmet is stated for the three chamber list cushion helmets, when driver wears the helmet, is made up of loudspeaker drum surface, earmuff pad, driver ear
Closed cavity, is residual noise control zone；Closed cavity is formed by the loudspeaker back side and earmuff shell, is noise reference area
Domain 1；Closed cavity is made up of earmuff shell, helmet body, driver head, is noise reference region 2.
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| CN113539228A (en) * | 2021-07-30 | 2021-10-22 | 北京安声浩朗科技有限公司 | Noise reduction parameter determination method and device, active noise reduction method and device |
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