CN111276117A - Active noise control method based on mixed frog-leaping algorithm - Google Patents

Active noise control method based on mixed frog-leaping algorithm Download PDF

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CN111276117A
CN111276117A CN202010077325.5A CN202010077325A CN111276117A CN 111276117 A CN111276117 A CN 111276117A CN 202010077325 A CN202010077325 A CN 202010077325A CN 111276117 A CN111276117 A CN 111276117A
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CN111276117B (en
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陈克安
王磊
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Abstract

The invention provides an active noise control method based on a mixed frog-leaping algorithm, which organically combines a cooperative search method based on frog groups with ANC (active noise control) and carries out information transmission according to group classification. In addition, the noise reduction scheme of the invention does not need secondary channel modeling, and can cope with sudden change of the secondary channel in the noise control process.

Description

Active noise control method based on mixed frog-leaping algorithm
Technical Field
The invention relates to the field of active noise control, and provides an active noise control method based on a mixed frog-leaping algorithm.
Background
Noise pollution is a particularly prominent problem in environmental pollution, and for processing Noise, there are two technical means, namely passive Noise Control (ANC) and Active Noise Control (ANC) respectively. The traditional noise control method belongs to passive noise control, and comprises the main steps of sound absorption treatment, sound insulation treatment, silencer use, vibration isolation, damping vibration attenuation and the like. In general, a passive noise control method has a good control effect on middle and high frequency noise, but has a poor control effect on low frequency noise. Unlike passive control, ANC techniques can effectively control low frequency noise. The mechanism of ANC is based on the principle of destructive interference of sound waves, whereby a primary sound source generates a desired signal and a secondary sound source generates a secondary signal of equal amplitude and opposite phase to the desired signal, which cancel each other out, thereby reducing noise.
The most commonly used algorithm in ANC is the FxLMS algorithm (Morgan D R. history, applications and future resolution of the FxLMS algorithm. IEEE Signal Processing Magazine2013,30(3): 172-. In an ANC system, the physical path from the secondary source to the error microphone is called the secondary path, and the links that make up the secondary path typically have the sound field, the electroacoustic device frequency response, and the electronics. In the conventional method, to complete an active control algorithm, an estimate of the secondary path transfer function must first be obtained. The FxLMS algorithm typically employs a secondary path offline modeling estimation. At this time, if the secondary path is changed, the performance of the ANC algorithm is degraded. In practical situations, the secondary path often appears as a time-varying system, so that the noise reduction effect using the conventional FxLMS algorithm is poor. Moreover, the FxLMS updates the weight coefficients according to a gradient algorithm, so that the problem of falling into a local optimal solution exists.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an ANC method based on a mixed frog Leaping Algorithm (SFLA). The SFLA is a collaborative search method based on frog groups, information is transmitted according to the group classification, a search strategy of local search and global information mixing is provided, a global optimal solution can be obtained in the search process, and a better noise reduction effect can be obtained compared with the conventional FxLMS algorithm. And the SFLA-based noise reduction scheme does not need secondary channel modeling, and can deal with sudden changes of the secondary channel in the noise control process.
The technical scheme of the invention is as follows:
the active noise control method based on the mixed frog-leaping algorithm is characterized by comprising the following steps: the method comprises the following steps:
step 1: an active controller of the active noise control system randomly generates N control filter weight coefficients, the weight coefficient of each control filter is used as a single frog individual, the weight coefficients of all N control filters form a frog group, and N is the scale of the group;
step 2: an active controller of the active noise control system selects a certain individual from the current population and enters step 3;
and step 3: the primary sound source of the active noise control system emits noise, the reference sensor picks up the reference signal x (n) as the input of the control filter, and simultaneously forms the expected signal d (n) at the error sensor; in continuous M sampling periods of an active noise control system, an active controller takes an individual entering the active noise control system as a weight coefficient of a control filter, a reference signal x (n) is filtered by the control filter to obtain a secondary signal y (n), and the secondary signal drives a secondary sound source to generate a cancellation signal s (n); at the error sensor, the expected signal d (n) is superposed with the offset signal s (n) to generate an error signal e (n), wherein e (n) is the error signal obtained by the active noise control system in each sampling period; calculating the mean square value of error signals obtained by the active noise control system in the M sampling periods
Figure BDA0002378853010000021
In the formula JiRepresents the mean square error value, t, of the ith frogiA start sampling time representing the frog individual as a control filter weight coefficient;
and 4, step 4: after M sampling periods corresponding to one individual are finished, the active controller selects a new individual from the current population again and enters step 3, and after M × N sampling periods, the fitness corresponding to all N frog individuals in the current population is obtained; wherein, the smaller the mean square error value corresponding to the individual is, the higher the fitness is;
and 5: dividing individuals in the current population into m sub-populations, executing an evolution process in each sub-population according to individual fitness or an error mean square value, after all the sub-populations finish one-time evolution, remixing each sub-population to form a new generation of population, and then returning to the step 2; and when the corresponding minimum mean square error value in the population converges to a set standard, obtaining an individual corresponding to the minimum mean square error value as a weight coefficient of a corresponding control filter of the current secondary channel of the active noise control system.
Further, the method also comprises the step 6: when detecting that the secondary path of the active noise control system changes, returning to the step 1, and initializing the population again; the basis for judging the change of the secondary path is as follows: the minimum error mean square value corresponding to the current generation population is larger than the minimum error mean square value corresponding to the previous generation population, and the difference value is larger than a set value.
Further, the manner of dividing the individuals in the population into m sub-populations in step 5 is as follows: sequencing the individuals in the population according to the mean square error value or fitness, and then circularly grouping the individuals to generate sub-populations, wherein each sub-population comprises N/m individuals; the cyclic grouping refers to: after sorting, the 1 st individual enters a first sub-population, the 2 nd individual enters a second sub-population, …, the mth individual enters a mth sub-population, the m +1 th individual enters the first sub-population, and so on until all individuals enter the sub-populations.
Further, the specific process of performing evolution inside each sub-population in step 5 is as follows:
step 5.1: setting the allowed maximum evolutionary times In each sub-population as Y, wherein In represents the current evolutionary times and the initial value is 0;
step 5.2: in the In-evolution, the individual W with the best fitness within the sub-population was usedbDirecting the individuals W with the worst fitnesswTo obtain the individual W with the worst fitnesswHas a moving distance of
D＝Rand()×(Wb-Ww)
In the formula, Rand () represents a random number from 0 to 1, and a new individual obtained after the individual with the worst fitness moves is
Ww'＝Ww+D
Step 5.3: if step 5.2 is able to produce a better fitness individual, the newly-fit individual is substituted for the least fitness individual WwAnd go to step 5.5, otherwise utilize WgInstead of WbRepeating the process of step 5.2, and entering step 5.4 after step 5.2 is completed;
step 5.4: if can produceIf an individual with better fitness is generated, the new individual is used for replacing the individual W with the worst fitnesswAnd step 5.5 is carried out, otherwise, a new individual is randomly generated to replace the individual W with the worst fitnessw；
Step 5.5: and if the current evolution number In is less than the maximum evolution number Y allowed by the sub-population, returning to the step 5.2.
Further, in step 1, if the length of the control filter is L, a single frog individual in the population is an L-dimensional variable, which indicates the current position of the individual.
Further, in step 5.2, Rand () is also an L-dimensional variable, and random numbers of each dimension are independent of each other.
Advantageous effects
(1) The ANC method based on the SFLA can realize global information exchange and deep search of local information, and avoid trapping in local extreme points, so that a global optimal point is searched, and the best noise reduction effect is obtained.
(2) The iterative process of the method weight coefficients does not involve filtering the-x signal, and therefore no sub-path modeling is required. In the case of a change in the secondary path, the system noise reduction performance is not affected.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1: schematic diagram of an SFLA-based ANC system;
FIG. 2: a weight coefficient iteration flow chart;
FIG. 3: evolution of individuals in the sub-population;
FIG. 4: arranging the initial population;
FIG. 5: a minimum mean square error convergence process;
FIG. 6: the secondary path undergoes a varying minimum mean square error convergence process.
Detailed Description
An ANC system based on the SFLA algorithm is shown in fig. 1. In the figure, x (n) is a reference signal picked up by a reference sensor in the ANC system, and is an input of a control filter. d (n) is the desired signal generated by the noise source at the error sensor in the ANC system. y (n) is a secondary signal calculated by the control filter, and the secondary signal drives a secondary sound source after passing through a power amplifier to generate a cancellation signal s (n) at the error sensor. The desired signal and the cancellation signal are superimposed to form an error signal e (n). P (z) and s (z) represent the primary and secondary paths, respectively. w is agThe control filter weight coefficient value representing the current generation is represented in SFLA as the location where the frog population of the current generation is located. It can be seen that the iteration of its weight coefficients does not require the filtered-x signal necessary in the FxLMS algorithm and therefore does not require sub-path modeling.
The SFLA-based algorithm only uses one same control filter weight coefficient within a certain time, the filter weight coefficients are updated generation by generation, and a new population is formed according to an adjustment strategy of the algorithm. The iterative steps of weights based on SFLA are shown in fig. 2. In order to be able to adapt to changes in the secondary path, no end criterion is set in the algorithm.
The method comprises the following specific steps:
(1) in the initial stage of ANC, an active controller of the ANC system randomly generates N control filter weight coefficients, the weight coefficient of each control filter is called a single frog individual, the weight coefficients of all N control filters are called a frog group, and N is the size of the group. Population initialization in controller
wg0＝{w1,w2,w3,...wN} (1)
And if the length of the control filter is set to be L, a single frog in the population is an L-dimensional variable which represents the current position of the frog.
The primary sound source of the ANC system emits noise and the reference sensor picks up the reference signal x (n) as input to the control filter, while the desired signal d (n) is formed at the error sensor. In M continuous sampling periods of the ANC system, the active controller selects a certain individual from the current population as a weight coefficient of the control filter. Then the secondary signal y (n) is obtained by filtering the reference signal x (n) through the control filter, and the secondary signal drives the secondary sound source to generate the cancellation signal s (n). At the error sensor, the desired signal d (n) is superimposed with the cancellation signal s (n) to produce an error signal e (n), i.e.
e(n)＝d(n)+s(n) (2)
e (n) is an error signal obtained in each sampling period of the ANC system; for the error signals obtained by the ANC system in M sampling periods, the mean value of the squares of the error signals is stored in the controller, and the mean value of the squares of the errors is used as the evaluation standard of the fitness of the currently selected individual frog, and the lower the mean value of the errors corresponding to the individual is, the higher the fitness is, and for example, the fitness can be taken as the reciprocal of the mean value of the errors. The concrete formula is
Figure BDA0002378853010000051
In the formula JiRepresents the mean square error value, t, of the ith frogiThe start sampling time of the individual frog as the control filter weight coefficient is shown.
After the M sampling periods are over, the active controller selects another frog individual in the group as the weight coefficient of the control filter, and it should be noted that, for different individuals, the corresponding sampling time lengths M should be consistent, so that the fitness of the different individuals can be correctly evaluated. After M × N sampling periods, the fitness of all N frog individuals in the colony is obtained.
In each generation group, all N individuals generate secondary signals as weight coefficients for controlling the filter. And respectively selecting different individuals in the group as weight coefficients of the control filter in respective M sampling periods, and recording the mean square value of the corresponding error signals, thereby obtaining the fitness values of the N individuals. The performance of the ith individual position is measured by the fitness, namely the smaller the mean square error value of the ith individual is, the higher the fitness is, the better the individual position is.
(2) For the whole populationThe ranking may be, for example, descending order of the N individuals according to fitness, and then cyclically grouping the frogs to generate sub-populations. The population is divided into m sub-populations: w1,W2,...WmThen each sub-population contains N/m frogs. And (3) enabling the sequenced 1 st frog to enter a first sub-population, enabling the 2 nd frog to enter a second sub-population, …, enabling the mth frog to enter the mth sub-population, enabling the m +1 th frog to enter the first sub-population, and repeating the steps until all individuals enter the sub-populations. The number m of the sub-populations needs to be reasonably selected. If the value of m is too large, there are fewer individuals in each sub-population, the communication of information within the sub-population is reduced, and the advantages of performing a local search are lost.
(3) After the populations are grouped, an evolution process is performed inside each sub-population, and the positions of individuals in the sub-populations are improved through evolution. And setting the maximum allowable evolution times In each sub-population as Y, wherein In represents the current evolution times and the initial value is 0. In each sub-population, with WbFrog showing the best performance, by WwRepresenting the worst performing frog. By WgRepresenting the frogs with the best overall population performance. In each evolution, the frog W with the best performance among the current sub-populations was utilizedbFrog W with the worst instructional performancew. The evolution steps of the individuals in the sub-population are shown in FIG. 3. The specific operation is as follows:
① adjusting the position of the worst frog in the sub-population with the best frog WbFrog W with the worst instructional performancew. The worst frog movement distance is
D＝Rand()×(Wb-Ww) (4)
Where Rand () represents a random number from 0 to 1. The new position after the worst frog movement is
Ww'＝Ww+D (5)
② if step ① is able to produce a better solution (i.e., using W)w' As the weight coefficient of the control filter, calculate the mean square value of the error in M sampling periods of the ANC system, if the mean square value of the error is better than WwCorresponding mean square errorValue), the worst performing frog W is replaced with the new position frogwProceed to step ④, otherwise utilize WgInstead of WbRepeat the process of step ① and proceed to step ③ after step ① is complete;
③ if a better solution can be produced, the worst performing frog W is replaced with the new position frogwStep ④ is entered, otherwise, a new individual is randomly generated to replace the frog W with the worst performancew；
④ if the current number of evolutionary times In is less than the maximum number of iterations allowed by the sub-population, return to step ①.
(4) After each sub-population has completed one evolution, the sub-populations are remixed to form a new generation of population, and then step (2) is resumed.
Under the condition that the secondary path is not changed, the frog population gradually converges to the optimal position, namely the minimum mean square error is obtained, and then the optimal noise reduction effect is obtained. If the secondary path suddenly changes, the population cannot adapt to the change and remains at a local minimum because all individual values are the same after convergence and randomness is lost. Therefore, if a change in the secondary path is detected, the population needs to be reinitialized. The basis for judging the change of the secondary path is as follows: if the whole population of the current generation is the best frog WgThe fitness value of the system is smaller than the optimal fitness value of the previous generation, namely the minimum mean square error of the current generation is larger than the minimum mean square error of the previous generation, and the difference value is larger than a set value, the current system is changed, at the moment, the controller judges that the secondary channel is changed, the population is reinitialized, and the evolution process is started.
The following detailed description of embodiments of the invention is intended to be illustrative, and not to be construed as limiting the invention.
(1) The primary path used in computer simulation is p (z) ═ z-6+0.5z-7-0.3z-8+0.6z-9The secondary path is S (z) ═ z-3+1.5z-4-0.5z-5. The primary noise is white noise with zero mean. Is provided withAnd if the length of the control filter is L-20, the frog is a 20-dimensional variable. The SFLA parameters were chosen as: the frog population scale N is 40, the number m of the sub-populations is 4, and the maximum evolution times Y allowed in the sub-populations is 10.
(2) The active controller randomly generates 40 sets of control filter weight coefficients, each set having a length of 20. Within a sample set consisting of currently M consecutive sample periods, the controller selects a certain individual as the control filter. The reference signal x (n) is used as the input of the controller, the secondary signal y (n) is obtained by filtering the reference signal x (n) through the control filter, and at the error sensor, the expected signal d (n) is superposed with the offset signal s (n) to generate the error signal e (n). The sampling length M for evaluating the individual fitness is set to 100. Storing the mean of the square of the error signal in the controller over the sample length
Figure BDA0002378853010000081
All individuals generate secondary signals as weight coefficients for the adaptive filter. And respectively selecting different individuals in the group from different sampling sets as filter weight coefficients and recording the mean square value of the corresponding error signals, thereby obtaining N mean square errors and evaluating the fitness values of the N individuals.
(3) The whole population is graded, the individuals are arranged in a descending order according to the fitness, and the arranged population is shown in fig. 4. The frogs are then cyclically grouped to generate 4 sub-populations. The 1 st frog after sorting entered the first sub-population, the 2 nd frog entered the second sub-population, …, and the 40 th frog entered the fourth sub-population.
(4) After the sub-populations are distributed, the evolution process is carried out in each sub-population in sequence, the position of the frog with the worst performance is adjusted, and in each evolution, the frog W with the best performance in the current sub-population is utilizedbFrog W with the worst instructional performancew. In the process of local search, the randomness of the change of each variable to the optimal direction is limited by taking the same Rand value for each dimension of the variable, so that the step moving distance formula of the low-fitness frog is improved. For each dimensionThe variable randomly generates different Rand values, and the moving distance of the worst frog is
Figure BDA0002378853010000082
In which the superscript j represents the jth dimension component of the parameter.
The maximum number of evolutionary allowed within each sub-population was 10. After the evolution of all 4 sub-populations is completed, the sub-populations are remixed to form a new generation of population. The process of group evolution is then repeated for the new generation population.
(5) Under the condition that the secondary path is kept unchanged, the population is subjected to stable evolution, all individuals gradually converge to the optimal position, and the convergence process of the minimum mean square error of each generation is shown in figure 5. It can be seen from the figure that in the initial stage of the algorithm, the convergence rate is fast and the minimum mean square error in the population is rapidly reduced. After the 50-generation population, the convergence process gradually stabilizes, and the positions of the frog individuals in the population are closer to each other and gradually approach the optimal solution.
(6) SFLA-based ANC methods do not require secondary path modeling, and therefore their performance is immune to changes in the secondary path. To verify this feature, the secondary pathway of the system is changed from the initial secondary pathway to s (z) z as the population evolves to 100 generations-2+1.5z-3-z-4. At this point, the minimum mean square error of the generation will increase instantaneously. And when the controller detects that the current secondary path is changed, the population is initialized again. The convergence process of the minimum mean square error is shown in fig. 6. It can be seen from the figure that the reinitialized population can converge to an optimal solution. The ANC method based on SFLA can adapt to the change of the system secondary path, ensuring the noise reduction effect.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.

Claims (6)

1. An active noise control method based on a mixed frog-leaping algorithm is characterized in that: the method comprises the following steps:
step 1: an active controller of the active noise control system randomly generates N control filter weight coefficients, the weight coefficient of each control filter is used as a single frog individual, the weight coefficients of all N control filters form a frog group, and N is the scale of the group;
step 2: an active controller of the active noise control system selects a certain individual from the current population and enters step 3;
and step 3: the primary sound source of the active noise control system emits noise, the reference sensor picks up the reference signal x (n) as the input of the control filter, and simultaneously forms the expected signal d (n) at the error sensor; in continuous M sampling periods of an active noise control system, an active controller takes an individual entering the active noise control system as a weight coefficient of a control filter, a reference signal x (n) is filtered by the control filter to obtain a secondary signal y (n), and the secondary signal drives a secondary sound source to generate a cancellation signal s (n); at the error sensor, the expected signal d (n) is superposed with the offset signal s (n) to generate an error signal e (n), wherein e (n) is the error signal obtained by the active noise control system in each sampling period; calculating the mean square value of error signals obtained by the active noise control system in the M sampling periods
Figure FDA0002378851000000011
In the formula JiRepresents the mean square error value, t, of the ith frogiA start sampling time representing the frog individual as a control filter weight coefficient;
and 4, step 4: after M sampling periods corresponding to one individual are finished, the active controller selects a new individual from the current population again and enters step 3, and after M × N sampling periods, the fitness corresponding to all N frog individuals in the current population is obtained; wherein, the smaller the mean square error value corresponding to the individual is, the higher the fitness is;
and 5: dividing individuals in the current population into m sub-populations, executing an evolution process in each sub-population according to individual fitness or an error mean square value, after all the sub-populations finish one-time evolution, remixing each sub-population to form a new generation of population, and then returning to the step 2; and when the corresponding minimum mean square error value in the population converges to a set standard, obtaining an individual corresponding to the minimum mean square error value as a weight coefficient of a corresponding control filter of the current secondary channel of the active noise control system.
2. The active noise control method based on the mixed frog-leaping algorithm according to claim 1, characterized in that: further comprising the step 6: when detecting that the secondary path of the active noise control system changes, returning to the step 1, and initializing the population again; the basis for judging the change of the secondary path is as follows: the minimum error mean square value corresponding to the current generation population is larger than the minimum error mean square value corresponding to the previous generation population, and the difference value is larger than a set value.
3. The active noise control method based on the mixed frog-leaping algorithm according to the claim 1 or 2, characterized in that: in step 5, the manner of dividing the individuals in the population into m sub-populations is as follows: sequencing the individuals in the population according to the mean square error value or fitness, and then circularly grouping the individuals to generate sub-populations, wherein each sub-population comprises N/m individuals; the cyclic grouping refers to: after sorting, the 1 st individual enters a first sub-population, the 2 nd individual enters a second sub-population, …, the mth individual enters a mth sub-population, the m +1 th individual enters the first sub-population, and so on until all individuals enter the sub-populations.
4. The active noise control method based on the mixed frog-leaping algorithm according to claim 3, characterized in that: the specific process of performing evolution inside each sub-population in step 5 is as follows:
step 5.1: setting the allowed maximum evolutionary times In each sub-population as Y, wherein In represents the current evolutionary times and the initial value is 0;
step 5.2: in the In-evolution, the individual W with the best fitness within the sub-population was usedbDirecting the individuals W with the worst fitnesswTo obtain the individual W with the worst fitnesswHas a moving distance of
D＝Rand()×(Wb-Ww)
In the formula, Rand () represents a random number from 0 to 1, and a new individual obtained after the individual with the worst fitness moves is
W'w＝Ww+D
Step 5.3: if step 5.2 is able to produce a better fitness individual, the newly-fit individual is substituted for the least fitness individual WwAnd go to step 5.5, otherwise utilize WgInstead of WbRepeating the process of step 5.2, and entering step 5.4 after step 5.2 is completed;
step 5.4: if a more well-adapted individual can be generated, the newly adapted individual W is replaced with the less well-adapted individual WwAnd step 5.5 is carried out, otherwise, a new individual is randomly generated to replace the individual W with the worst fitnessw；
Step 5.5: and if the current evolution number In is less than the maximum evolution number Y allowed by the sub-population, returning to the step 5.2.
5. The active noise control method based on the mixed frog-leaping algorithm according to claim 4, characterized in that: in step 1, if the length of the control filter is L, a single frog individual in the population is an L-dimensional variable, which represents the current position of the individual.
6. The active noise control method based on the mixed frog-leaping algorithm according to claim 5, characterized in that: in step 5.2, Rand () is also an L-dimensional variable, and random numbers of each dimension are independent of each other.
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