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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陈克安
王磊
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Abstract

本发明提出一种基于混合蛙跳算法的有源噪声控制方法,该方法将基于青蛙群体的协同搜索方法与ANC有机的相结合,按照种群分类进行信息传递,具有局部搜索和全局信息混合的搜索策略,能够在搜索过程中获得全局最优解,与现有FxLMS算法相比可以获得更好的降噪效果。并且本发明的降噪方案不需要次级通路建模,在噪声控制过程中,能够应对次级通路的突变。

Figure 202010077325

The invention proposes an active noise control method based on a hybrid frog leaping algorithm, which organically combines a frog group-based collaborative search method with ANC, performs information transmission according to population classification, and has local search and global information mixed search. The strategy can obtain the global optimal solution in the search process, and can obtain better noise reduction effect compared with the existing FxLMS algorithm. In addition, the noise reduction scheme of the present invention does not require modeling of the secondary path, and can cope with the sudden change of the secondary path during the noise control process.

Figure 202010077325

Description

一种基于混合蛙跳算法的有源噪声控制方法An Active Noise Control Method Based on Hybrid Frog Leaping Algorithm

技术领域technical field

本发明涉及有源噪声控制领域,提出了一种基于混合蛙跳算法的有源噪声控制方法。The invention relates to the field of active noise control, and proposes an active noise control method based on a hybrid frog leaping algorithm.

背景技术Background technique

噪声污染是环境污染中一个特别突出的问题,对于噪声的处理,通常有两种技术手段,分别是无源噪声控制和有源噪声控制(Active Noise Control,ANC)技术。传统的噪声控制方法属于无源噪声控制,其主要方法包括吸声处理、隔声处理、使用消声器、振动隔离、阻尼减振等。一般情况下,无源噪声控制方法对中高频噪声的控制效果较好,而对低频噪声效果不佳。与无源控制不同,ANC技术能够有效地控制低频噪声。ANC的机理是根据声波的相消性干涉原理,初级声源产生期望信号,次级声源便产生一个与期望信号幅度相等、相位相反的次级信号,二者相互抵消,从而降低噪声。Noise pollution is a particularly prominent problem in environmental pollution. There are usually two technical means for noise treatment, namely passive noise control and Active Noise Control (ANC) technology. The traditional noise control method belongs to passive noise control, and its main methods include sound absorption treatment, sound insulation treatment, the use of mufflers, vibration isolation, damping and vibration reduction. In general, the passive noise control method has better control effect on medium and high frequency noise, but has poor effect on low frequency noise. Unlike passive control, ANC technology can effectively control low frequency noise. The mechanism of ANC is based on the principle of destructive interference of sound waves. The primary sound source generates the desired signal, and the secondary sound source generates a secondary signal with the same amplitude and opposite phase as the desired signal. The two cancel each other out, thereby reducing noise.

ANC中最常用的算法是FxLMS算法(Morgan D R.History,applications andsubsequent development of the FxLMS algorithm.IEEE Signal Processing Magazine2013,30(3):172-176)。在ANC系统中,从次级声源到误差传声器之间的物理通路称为次级通路,组成次级通路的环节通常有声场、电声器件频响和电子线路。在传统方法中,要完成一个有源控制算法,必须首先得到次级通路传递函数的估计值。FxLMS算法通常采用次级通路离线建模估计。这时,如果次级通路发生变化,会导致ANC算法性能下降。而在实际情况中,次级通路经常表现为一个时变系统,所以采用传统FxLMS算法的降噪效果较差。而且FxLMS是依据梯度算法对权系数进行更新的,会存在陷入局部最优解的问题。The most commonly used algorithm in ANC is the FxLMS algorithm (Morgan D R. History, applications and subsequent development of the FxLMS algorithm. IEEE Signal Processing Magazine 2013, 30(3): 172-176). In the ANC system, the physical path from the secondary sound source to the error microphone is called the secondary path, and the links that make up the secondary path usually include the sound field, the frequency response of electro-acoustic devices and electronic circuits. In the traditional method, to complete an active control algorithm, an estimate of the transfer function of the secondary path must first be obtained. The FxLMS algorithm usually adopts offline modeling and estimation of secondary paths. At this time, if the secondary path changes, the performance of the ANC algorithm will be degraded. In practice, the secondary path often behaves as a time-varying system, so the noise reduction effect of the traditional FxLMS algorithm is poor. Moreover, FxLMS updates the weight coefficients according to the gradient algorithm, and there is a problem of falling into a local optimal solution.

发明内容SUMMARY OF THE INVENTION

为解决现有技术存在的问题,本发明提出了一种基于混合蛙跳算法(ShuffledFrog Leaping Algorithm,SFLA)的ANC方法。SFLA是一种基于青蛙群体的协同搜索方法,该方法按照种群分类进行信息传递,具有局部搜索和全局信息混合的搜索策略,能够在搜索过程中获得全局最优解,与现有FxLMS算法相比可以获得更好的降噪效果。并且基于SFLA的降噪方案不需要次级通路建模,在噪声控制过程中,能够应对次级通路的突变。In order to solve the problems existing in the prior art, the present invention proposes an ANC method based on the Shuffled Frog Leaping Algorithm (SFLA). SFLA is a collaborative search method based on frog populations. This method transmits information according to population classification. It has a search strategy that combines local search and global information, and can obtain the global optimal solution during the search process. Compared with the existing FxLMS algorithm Can get better noise reduction effect. And the SFLA-based noise reduction scheme does not require secondary path modeling, and can cope with the sudden change of the secondary path during the noise control process.

本发明的技术方案为:The technical scheme of the present invention is:

所述一种基于混合蛙跳算法的有源噪声控制方法,其特征在于:包括以下步骤:Described a kind of active noise control method based on hybrid frog leap algorithm, is characterized in that: comprises the following steps:

步骤1:有源噪声控制系统的有源控制器随机产生N个控制滤波器权系数,每个控制滤波器的权系数作为一个单独的青蛙个体,全部N个控制滤波器的权系数组成青蛙群体,N为该群体的规模;Step 1: The active controller of the active noise control system randomly generates N control filter weight coefficients, the weight coefficient of each control filter acts as a separate frog individual, and the weight coefficients of all N control filters form a frog group , N is the size of the group;

步骤2:有源噪声控制系统的有源控制器从当前种群中选择某个个体进入步骤3;Step 2: The active controller of the active noise control system selects an individual from the current population and enters step 3;

步骤3:有源噪声控制系统的初级声源发出噪声,参考传感器拾取到参考信号x(n)作为控制滤波器的输入,同时在误差传感器处形成期望信号d(n);在有源噪声控制系统的连续M个采样周期内,有源控制器以进入本步骤的个体作为控制滤波器的权系数,参考信号x(n)通过该控制滤波器滤波获得次级信号y(n),次级信号驱动次级声源产生抵消信号s(n);在误差传感器处,期望信号d(n)与抵消信号s(n)叠加产生误差信号e(n),e(n)是有源噪声控制系统在每个采样周期内得到的误差信号;计算有源噪声控制系统在该M个采样周期所得到的误差信号均方值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 the desired signal d(n) is formed at the error sensor; in the active noise control In the continuous M sampling periods of the system, the active controller uses the individual entering this step as the weight coefficient of the control filter, the reference signal x(n) is filtered by the control filter to obtain the secondary signal y(n), and the secondary signal y(n) is obtained by filtering the reference signal x(n). The signal drives the secondary sound source to generate the cancellation signal s(n); at the error sensor, the desired signal d(n) and the cancellation signal s(n) are superimposed to generate the error signal e(n), e(n) is the active noise control The error signal obtained by the system in each sampling period; calculate the mean square value of the error signal obtained by the active noise control system in the M sampling periods

Figure BDA0002378853010000021
Figure BDA0002378853010000021

式中Ji表示第i个青蛙个体的误差均方值,ti表示该青蛙个体作为控制滤波器权系数的开始采样时刻;In the formula, J i represents the mean square value of the error of the ith frog individual, and t i represents the start sampling time of the frog individual as the control filter weight coefficient;

步骤4:当一个个体对应的M个采样周期结束后,有源控制器从当前种群中再次选择一个新的个体进入步骤3,如此经过M*N个采样周期后,得到当前种群中所有N个青蛙个体对应的适应度;其中个体对应的误差均方值越小,则其适应度越高;Step 4: When the M sampling periods corresponding to an individual are over, the active controller selects a new individual from the current population to enter Step 3. After M*N sampling periods, all N samples in the current population are obtained. The fitness corresponding to the individual frog; the smaller the error mean square value corresponding to the individual, the higher the fitness;

步骤5:对当前种群内的个体分成m个子种群,在每个子种群内部依据个体适应度或误差均方值执行进化过程,在所有子种群完成一次进化之后,各个子种群重新混合,形成新一代种群,然后返回步骤2;当种群中对应的最小误差均方值收敛到设定标准后,得到最小误差均方值对应的个体作为有源噪声控制系统当前次级通路对应控制滤波器的权系数。Step 5: Divide the individuals in the current population into m sub-populations, and execute the evolution process within each sub-population according to the individual fitness or the mean square value of the error. After all sub-populations complete an evolution, each sub-population is remixed to form a new generation. population, and then return to step 2; when the corresponding minimum error mean square value in the population converges to the set standard, the individual corresponding to the minimum error mean square value is obtained as the weight coefficient of the control filter corresponding to the current secondary path of the active noise control system .

进一步的,还包括步骤6:当检测到有源噪声控制系统的次级通路发生变化时,返回步骤1,重新对种群进行初始化;其中判断次级通路发生变化的依据是:当前代种群对应的最小误差均方值大于上一代种群对应的最小误差均方值,且差值大于设定值。Further, it also includes step 6: when it is detected that the secondary path of the active noise control system changes, return to step 1, and re-initialize the population; wherein the basis for judging the change of the secondary path is: the corresponding to the current generation population. The minimum error mean square value is greater than the minimum error mean square value corresponding to the previous generation population, and the difference is greater than the set value.

进一步的,步骤5中将种群内的个体分成m个子种群的方式为:将种群内的个体按照误差均方值或适应度进行排序,然后将个体循环分组生成子种群,每个子种群中包含N/m个个体;所述循环分组指:排序之后的第1个个体进入到第一个子种群,第2个个体进入到第二个子种群,…,第m个个体进入到第m个子种群,第m+1个个体又进入到第一个子种群,依次类推,直到所有个体进入到子种群。Further, in step 5, the individuals in the population are divided into m sub-populations as follows: the individuals in the population are sorted according to the mean square value of error or fitness, and then the individuals are grouped cyclically to generate sub-populations, each sub-population contains N /m individuals; the cyclic grouping refers to: the first individual after sorting enters the first sub-population, the second individual enters the second sub-population, ..., the m-th individual enters the m-th sub-population, The m+1th individual enters the first subpopulation again, and so on, until all individuals enter the subpopulation.

进一步的,步骤5中在每个子种群内部执行进化的具体过程为:Further, the specific process of performing evolution within each subpopulation in step 5 is as follows:

步骤5.1:设置每个子种群中允许的最大进化次数为Y,In表示当前进化次数,初始值为0;Step 5.1: Set the maximum number of evolutions allowed in each subpopulation as Y, In represents the current number of evolutions, and the initial value is 0;

步骤5.2:在第In次进化中,利用子种群内适应度最好的个体Wb指导适应度最差的个体Ww,得到适应度最差个体Ww的移动距离为Step 5.2: In the In-th evolution, the individual W b with the best fitness in the sub-population is used to guide the individual W w with the worst fitness, and the moving distance of the individual W w with the worst fitness is obtained:

D=Rand()×(Wb-Ww)D=Rand()×(W b -W w )

式中Rand()表示从0到1的随机数,适应度最差个体移动后得到的新个体为In the formula, Rand() represents a random number from 0 to 1, and the new individual obtained after the individual with the worst fitness moves is

Ww'=Ww+DW w '=W w +D

步骤5.3:如果步骤5.2能够产生一个适应度更好的个体,则用新个体取代适应度最差的个体Ww,并进入步骤5.5,否则利用Wg代替Wb,重复步骤5.2的过程,并在步骤5.2完成后进入步骤5.4;Step 5.3: If step 5.2 can generate an individual with better fitness, replace the individual W w with the worst fitness with a new individual, and go to step 5.5, otherwise use W g to replace W b , repeat the process of step 5.2, and After the completion of step 5.2, go to step 5.4;

步骤5.4:如果能够产生一个适应度更好的个体,则用新个体取代适应度最差的个体Ww,并进入步骤5.5,否则,随机产生一个新个体取代适应度最差的个体WwStep 5.4: If an individual with better fitness can be generated, replace the individual W w with the worst fitness with a new individual, and go to step 5.5, otherwise, randomly generate a new individual to replace the individual W w with the worst fitness;

步骤5.5:如果当前进化次数In小于子种群允许的最大进化次数Y,则返回步骤5.2。Step 5.5: If the current evolution times In is less than the maximum evolution times Y allowed by the subpopulation, go back to step 5.2.

进一步的,步骤1中,若控制滤波器长度为L,则种群中单个青蛙个体为L维变量,表示个体当前所处的位置。Further, in step 1, if the length of the control filter is L, then a single frog individual in the population is an L-dimensional variable, representing the current position of the individual.

进一步的,步骤5.2中,Rand()也为L维变量,且各维的随机数相互独立。Further, in step 5.2, Rand() is also an L-dimensional variable, and the random numbers of each dimension are independent of each other.

有益效果beneficial effect

(1)本发明基于SFLA的ANC方法能够实现全局性的信息交换和局部信息的深度搜索,避免陷入局部极值点,从而向全局最优点进行搜索,获得最好的降噪效果。(1) The SFLA-based ANC method of the present invention can realize global information exchange and local information depth search, avoid falling into local extreme points, so as to search for the global optimum point and obtain the best noise reduction effect.

(2)该方法权系数的迭代过程不包含滤波-x信号,因此不需要进行次级通路建模。在次级通路发生变化的情况下,系统降噪性能不会受到影响。(2) The iterative process of the weight coefficients of this method does not include filtering the -x signal, so no secondary path modeling is required. In the case of changes in the secondary path, the noise reduction performance of the system is not affected.

本发明的附加方面和优点将在下面的描述中部分给出,部分将从下面的描述中变得明显,或通过本发明的实践了解到。Additional aspects and advantages of the present invention will be set forth, in part, from the following description, and in part will be apparent from the following description, or may be learned by practice of the invention.

附图说明Description of drawings

本发明的上述和/或附加的方面和优点从结合下面附图对实施例的描述中将变得明显和容易理解,其中:The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of embodiments taken in conjunction with the accompanying drawings, wherein:

图1:基于SFLA的ANC系统示意图;Figure 1: Schematic diagram of ANC system based on SFLA;

图2:权系数迭代流程图;Figure 2: Weight coefficient iteration flow chart;

图3:子种群中个体进化步骤;Figure 3: Individual evolution steps in subpopulations;

图4:排列后的初始种群;Figure 4: Initial population after permutation;

图5:最小均方误差收敛过程;Figure 5: Minimum mean square error convergence process;

图6:次级通路发生变化的最小均方误差收敛过程。Figure 6: Minimum mean square error convergence process for secondary path changes.

具体实施方式Detailed ways

基于SFLA算法的ANC系统如图1所示。图中x(n)为ANC系统中参考传感器拾取的参考信号,是控制滤波器的输入。d(n)为噪声源在ANC系统中误差传感器处产生的期望信号。y(n)为控制滤波器计算出的次级信号,经过功率放大器后驱动次级声源,在误差传感器处产生抵消信号s(n)。期望信号和抵消信号叠加后形成误差信号e(n)。P(z)和S(z)分别代表初级通路和次级通路。wg表示当前代的控制滤波器权系数值,在SFLA中表示为当前代的青蛙群体所处的位置。由图中可以看出,其权系数的迭代并不需要FxLMS算法中所必需的滤波-x信号,因此不需要进行次级通路建模。The ANC system based on the SFLA algorithm is shown in Figure 1. In the figure, x(n) is the reference signal picked up by the reference sensor in the ANC system, which is the input of the control filter. d(n) is the desired signal generated by the noise source at the error sensor in the ANC system. y(n) is the secondary signal calculated by the control filter. After passing through the power amplifier, the secondary sound source is driven, and the cancellation signal s(n) is generated 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 pathways, respectively. w g represents the weight coefficient value of the control filter of the current generation, which is expressed as the position of the frog population of the current generation in SFLA. It can be seen from the figure that the iteration of its weight coefficients does not require the filtering-x signal necessary in the FxLMS algorithm, so secondary path modeling is not required.

本发明基于SFLA的算法在一定时间内只使用一个相同的控制滤波器权系数,滤波器权系数是逐代更新的,根据算法的调整策略形成新的种群。基于SFLA的权系数迭代步骤如图2所示。为了能够适应次级通路的变化,在算法中没有设置结束标准。The SFLA-based algorithm of the present invention only uses one identical control filter weight coefficient within a certain period of time, the filter weight coefficient is updated generation by generation, and a new population is formed according to the adjustment strategy of the algorithm. The iterative steps of the weight coefficient based on SFLA are shown in Figure 2. In order to be able to adapt to the change of secondary path, no end criterion is set in the algorithm.

具体步骤如下:Specific steps are as follows:

(1)在ANC开始阶段,ANC系统的有源控制器随机产生N个控制滤波器权系数,每个控制滤波器的权系数称为一个单独的青蛙个体,全部N个控制滤波器的权系数称为青蛙群体,N为该群体的规模。控制器中种群初始化为(1) At the beginning of ANC, the active controller of the ANC system randomly generates N control filter weight coefficients, the weight coefficient of each control filter is called a separate frog individual, and the weight coefficients of all N control filters are is called a frog colony, and N is the size of the colony. The population in the controller is initialized as

wg0={w1,w2,w3,...wN} (1)w g0 ={w 1 ,w 2 ,w 3 ,...w N } (1)

设控制滤波器长度为L,则种群中单个青蛙个体为L维变量,表示青蛙当前所处的位置。Assuming that the length of the control filter is L, a single frog individual in the population is an L-dimensional variable, representing the current position of the frog.

ANC系统的初级声源发出噪声,参考传感器拾取到参考信号x(n)作为控制滤波器的输入,同时在误差传感器处形成期望信号d(n)。在ANC系统的连续M个采样周期内,有源控制器从当前种群中选择某个个体作为控制滤波器的权系数。则由参考信号x(n)通过该控制滤波器滤波获得次级信号y(n),次级信号驱动次级声源产生抵消信号s(n)。在误差传感器处,期望信号d(n)与抵消信号s(n)叠加产生误差信号e(n),即The primary sound source of the ANC system emits noise, the reference sensor picks up the reference signal x(n) as the input to the control filter, and the desired signal d(n) is formed at the error sensor. In the continuous M sampling period of the ANC system, the active controller selects an individual from the current population as the 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 generate the error signal e(n), that is,

e(n)=d(n)+s(n) (2)e(n)=d(n)+s(n) (2)

e(n)是ANC系统的每个采样周期内得到的误差信号;对于ANC系统在M个采样周期所得到的误差信号,将误差信号平方的均值存储在控制器中,作为当前所选择的青蛙个体的适应度的评价标准,个体对应的误差均方值越小,则其适应度越高,例如可以取适应度为误差均方值的倒数。具体公式为e(n) is the error signal obtained in each sampling period of the ANC system; for the error signal obtained by the ANC system in M sampling periods, the mean value of the square of the error signal is stored in the controller as the currently selected frog The evaluation standard of the fitness of an individual, the smaller the mean square value of the error corresponding to the individual is, the higher the fitness is. For example, the fitness can be taken as the reciprocal of the mean square value of the error. The specific formula is

Figure BDA0002378853010000051
Figure BDA0002378853010000051

式中Ji表示第i个青蛙个体的误差均方值,ti表示该青蛙个体作为控制滤波器权系数的开始采样时刻。In the formula, J i represents the mean square value of the error of the ith frog individual, and t i represents the start sampling time of the frog individual as the control filter weight coefficient.

在该M个采样周期结束后,有源控制器选择群体中的另外一个青蛙个体作为控制滤波器的权系数,需要注意的是,对于不同个体,其对应的采样时间长度M应当是一致的,这样才能正确评价不同个体的适应度。如此经过M*N个采样周期后,得到群体中所有N个青蛙个体的适应度。After the M sampling periods, the active controller selects another frog individual in the group as the weight coefficient of the control filter. It should be noted that for different individuals, the corresponding sampling time length M should be the same. In this way, the fitness of different individuals can be correctly evaluated. In this way, after M*N sampling periods, the fitness of all N frog individuals in the group is obtained.

在每一代群体中,所有N个个体都要作为控制滤波器的权系数产生次级信号。在各自M个采样周期内分别选取群体中的不同个体作为控制滤波器权系数并记录对应的误差信号的均方值,由此,获得N个个体的适应度值。利用适应度的大小衡量第i个个体位置性能的好坏,即第i个个体均方误差值越小,其适应度越高,则该个体位置越优。In each generation of the population, all N individuals are used as the weights of the control filter to generate secondary signals. In the respective M sampling periods, different individuals in the population are selected as the control filter weight coefficients, and the mean square value of the corresponding error signal is recorded, thereby obtaining the fitness values of N individuals. The fitness of the ith individual is used to measure the performance of the ith individual, that is, the smaller the mean square error value of the ith individual, the higher the fitness, and the better the individual position.

(2)对整个种群划分等级,例如可以按照适应度对N个个体进行降序排列,然后将青蛙循环分组生成子种群。将种群分成m个子种群:W1,W2,...Wm,则每个子种群中包含N/m个青蛙。排序之后的第1只青蛙进入到第一个子种群,第2只青蛙进入到第二个子种群,…,第m只青蛙进入到第m个子种群,第m+1只青蛙又进入到第一个子种群,依次类推,直到所有个体进入到子种群。需要合理选择子种群的个数m。如果m值太大,则每个子种群中的个体就会比较少,会减少子种群内信息的交流,进行局部搜索的优点就会丢失。(2) Classify the entire population, for example, N individuals can be sorted in descending order according to their fitness, and then the frogs are grouped cyclically to generate sub-populations. Divide the population into m sub-populations: W 1 , W 2 ,...W m , then each sub-population contains N/m frogs. After sorting, the first frog enters the first sub-population, the second frog enters the second sub-population, ..., the m-th frog enters the m-th sub-population, and the m+1-th frog enters the first sub-population again. subpopulations, and so on, until all individuals enter the subpopulation. The number m of subpopulations needs to be selected reasonably. If the value of m is too large, there will be fewer individuals in each sub-population, which will reduce the exchange of information within the sub-population, and the advantages of local search will be lost.

(3)将种群分组后,在每个子种群内部执行进化过程,通过进化,使得子种群中的个体位置获得改善。设置每个子种群中允许的最大进化次数为Y,In表示当前进化次数,初始值为0。在每个子种群中,用Wb表示性能最好的青蛙,用Ww表示性能最差的青蛙。用Wg表示整个种群性能最好的青蛙。在每一次进化中,利用当前子种群中性能最好的青蛙Wb指导性能最差的青蛙Ww。子种群中个体进化步骤如图3所示。具体操作如下:(3) After the population is grouped, the evolution process is performed within each sub-population, and the individual positions in the sub-population are improved through evolution. Set the maximum number of evolutions allowed in each subpopulation as Y, In represents the current number of evolutions, and the initial value is 0. In each subpopulation, the best-performing frog is denoted by W b and the worst-performing frog is denoted by W w . Denote the best performing frog in the entire population by W g . In each evolution, the best performing frog W b in the current subpopulation is used to guide the worst performing frog W w . The individual evolution steps in the subpopulation are shown in Figure 3. The specific operations are as follows:

①调整性能最差青蛙的位置。利用子种群内性能最好的青蛙Wb指导性能最差的青蛙Ww。最差青蛙的移动距离为① Adjust the position of the worst performing frog. Use the best-performing frog W b in the subpopulation to guide the worst-performing frog W w . The distance the worst frog can move is

D=Rand()×(Wb-Ww) (4)D=Rand()×(W b -W w ) (4)

式中Rand()表示从0到1的随机数。性能最差青蛙移动后的新位置为where Rand() represents a random number from 0 to 1. The new position of the worst performing frog after moving is

Ww'=Ww+D (5)W w '=W w +D (5)

②如果步骤①能够产生一个更好的解(即采用Ww'作为控制滤波器权系数,计算ANC系统M个采样周期内的误差均方值,如果该误差均方值优于Ww对应的误差均方值),则用新位置的青蛙取代性能最差的青蛙Ww,进入步骤④,否则利用Wg代替Wb,重复步骤①的过程,并在步骤①完成后进入步骤③;②If step ① can produce a better solution (that is, use Ww ' as the control filter weight coefficient, 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 the corresponding value of Ww Error mean square value), then replace the worst frog W w with the frog in the new position, go to step 4, otherwise use W g to replace W b , repeat the process of step ①, and go to step ③ after step ① is completed;

③如果能够产生一个更好的解,则用新位置的青蛙取代性能最差的青蛙Ww,进入步骤④,否则,随机产生一个新个体取代性能最差的青蛙Ww③ If a better solution can be generated, replace the frog W w with the worst performance with the frog in the new position, and go to step ④, otherwise, randomly generate a new individual to replace the frog W w with the worst performance;

④如果当前进化次数In小于子种群允许的最大迭代次数,则返回步骤①。④ If the current number of evolutions In is less than the maximum number of iterations allowed by the subpopulation, go back to step ①.

(4)在每个子种群完成一次进化之后,各个子种群重新混合,形成新一代种群,然后重新开始步骤(2)。(4) After each subpopulation completes one evolution, the subpopulations are remixed to form a new generation of populations, and then step (2) is started again.

在次级通路不变的情况下,青蛙种群逐渐收敛到最佳位置,即获得最小的均方误差,这时将获得最佳的降噪效果。如果次级通路突然发生变化,种群无法适应变化而保持在局部最小值,因为在收敛后所有个体的值相同,随机性丢失。因此,如果检测到次级通路发生变化,则需要重新对种群进行初始化。判断次级通路发生变化的依据是:如果当前代的整个种群最好的青蛙Wg的适应度值小于上一代的最佳适应度值,即当前代的最小均方误差要大于上一代的最小均方误差,且差值大于设定值,则意味着当前系统发生了变化,此时控制器判断次级通路发生了变化,则重新初始化种群,开始进化过程。Under the condition that the secondary path remains unchanged, the frog population gradually converges to the best position, that is, the minimum mean square error is obtained, and the best noise reduction effect will be obtained at this time. If the secondary pathway changes suddenly, the population cannot adapt to the change and remains at a local minimum because after convergence all individuals have the same value and randomness is lost. Therefore, if a change in the secondary pathway is detected, the population needs to be re-initialized. The basis for judging the change of the secondary pathway is: if the fitness value of the best frog W g in the entire population of the current generation is less than the best fitness value of the previous generation, that is, the minimum mean square error of the current generation is greater than the minimum value of the previous generation. If the mean square error is greater than the set value, it means that the current system has changed. At this time, the controller judges that the secondary path has changed, then reinitializes the population and starts the evolution process.

下面详细描述本发明的实施例,所述实施例是示例性的,旨在用于解释本发明,而不能理解为对本发明的限制。The embodiments of the present invention are described in detail below, and the embodiments are exemplary and intended to explain the present invention, but should not be construed as a limitation of the present invention.

(1)在计算机仿真中使用的初级通路为P(z)=z-6+0.5z-7-0.3z-8+0.6z-9,次级通路为S(z)=z-3+1.5z-4-0.5z-5。初级噪声为零均值的白噪声。设置控制滤波器长度为L=20,则青蛙个体为20维变量。SFLA参数的选择为:青蛙群体规模N=40,子种群个数m=4,子种群内允许最大进化次数Y=10。(1) The primary path used in the computer simulation is P(z)=z -6 +0.5z -7 -0.3z -8 +0.6z -9 , and the secondary path is S(z)=z -3 +1.5 z -4 -0.5z -5 . Primary noise is white noise with zero mean. Set the length of the control filter to L=20, and the frog individual is a 20-dimensional variable. The selection of SFLA parameters is: frog group size N = 40, the number of sub-populations m = 4, and the maximum allowed evolution times in the sub-population Y = 10.

(2)有源控制器随机产生40组控制滤波器权系数,每组长度为20。在当前连续M个采样周期组成的采样集合内,控制器选择某个个体作为控制滤波器。参考信号x(n)作为控制器的输入,则由参考信号x(n)通过该控制滤波器滤波获得次级信号y(n),在误差传感器处,期望信号d(n)与抵消信号s(n)叠加产生误差信号e(n)。设置评价个体适应度的采样长度M=100。在该采样长度内,将误差信号平方的均值存储在控制器中(2) The active controller randomly generates 40 groups of control filter weight coefficients, and the length of each group is 20. In the current sampling set consisting of M consecutive sampling periods, the controller selects an individual as the control filter. The reference signal x(n) is used as the input of the controller, then the secondary signal y(n) is obtained by filtering the reference signal x(n) through the control filter. At the error sensor, the expected signal d(n) and the cancellation signal s (n) The superposition produces an error signal e(n). Set the sampling length M=100 for evaluating individual fitness. Over this sample length, store the mean of the squared error signal in the controller

Figure BDA0002378853010000081
Figure BDA0002378853010000081

所有的个体都要作为自适应滤波器的权系数产生次级信号。在不同采样集合内分别选取群体中的不同个体作为滤波器权系数并记录对应的误差信号的均方值,由此,获得N个均方误差,评价N个个体的适应度值。All individuals are used as weights of the adaptive filter to generate secondary signals. In different sampling sets, different individuals in the population are selected as filter weight coefficients and the mean square value of the corresponding error signal is recorded, thereby obtaining N mean square errors and evaluating the fitness values of N individuals.

(3)对整个种群划分等级,按照适应度对个体进行降序排列,排列后的种群如图4所示。然后将青蛙循环分组生成4个子种群。排序之后的第1只青蛙进入到第一个子种群,第2只青蛙进入到第二个子种群,…,第40只青蛙进入到第四个子种群。(3) Classify the entire population, and arrange the individuals in descending order according to their fitness. The sorted population is shown in Figure 4. The frogs are then grouped cyclically to generate 4 subpopulations. After sorting, the first frog enters the first sub-population, the second frog enters the second sub-population, ..., the 40th frog enters the fourth sub-population.

(4)在子种群分配完毕后,各个子种群内依次进行进化过程,调整性能最差青蛙的位置,在每一次进化中,利用当前子种群中性能最好的青蛙Wb指导性能最差的青蛙Ww。在局部搜索的过程中,对于变量的每一维取相同的Rand值会限制各个变量向最优方向变化的随机性,因此改善低适应度青蛙的步移动距离公式。对每一维变量随机生成不同的Rand值,则最差青蛙的移动距离为(4) After the sub-population is allocated, the evolution process is carried out in each sub-population in turn, and the position of the frog with the worst performance is adjusted. In each evolution, the frog W b with the best performance in the current sub-population is used to guide the frog with the worst performance. Frog W w . In the process of local search, taking the same Rand value for each dimension of the variable will limit the randomness of each variable changing in the optimal direction, so the step moving distance formula of the low-fitness frog is improved. Randomly generate different Rand values for each dimension variable, then the moving distance of the worst frog is

Figure BDA0002378853010000082
Figure BDA0002378853010000082

式中上标j代表参数的第j维分量。In the formula, the superscript j represents the jth dimension component of the parameter.

对于每个子种群内允许进行的最大进化次数为10。在4个子种群都完成进化之后,各个子种群重新混合,形成新一代种群。然后对新一代种群重复分组进化的过程。The maximum number of evolutions allowed within each subpopulation is 10. After the evolution of the four subpopulations is completed, the subpopulations are remixed to form a new generation of populations. The process of group evolution is then repeated for a new generation of populations.

(5)在次级通路保持不变的情况下,种群进行稳定的进化,所有的个体逐渐收敛到最佳位置,每代最小均方误差的收敛过程如图5所示。由图中可以看出,在算法的开始阶段,收敛速度很快,种群中最小的均方误差迅速降低。在50代群体之后,收敛过程逐渐平稳,此时种群中青蛙个体的位置距离比较近,逐渐接近于最优解。(5) Under the condition that the secondary pathway remains unchanged, the population evolves steadily, and all individuals gradually converge to the best position. 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 speed is very fast, and the minimum mean square error in the population decreases rapidly. After 50 generations of the population, the convergence process is gradually stable. At this time, the frogs in the population are relatively close, and gradually approach the optimal solution.

(6)基于SFLA的ANC方法不需要次级通路建模,因此其性能对次级通路的变化免疫。为了验证该特征,在种群进化到100代时,改变系统的次级通路,由初始的次级通路改为S(z)=z-2+1.5z-3-z-4。这时,该代的最小均方误差将瞬间增大。控制器检测到当前次级通路发生变化,则重新对种群进行初始化。最小均方误差的收敛过程如图6所示。从图中可以看出重新初始化的种群可以收敛到最优解。则基于SFLA的ANC方法能够适应系统次级通路的变化,保证降噪效果。(6) The SFLA-based ANC method does not require secondary pathway modeling, so its performance is immune to changes in secondary pathways. In order to verify this feature, when the population evolves to 100 generations, the secondary pathway of the system is changed from the initial secondary pathway to S(z)=z -2 +1.5z -3 -z -4 . At this time, the minimum mean square error of the generation will increase instantaneously. When the controller detects that the current secondary path has changed, it re-initializes the population. The convergence process of the minimum mean square error is shown in Figure 6. It can be seen from the figure that the reinitialized population can converge to the optimal solution. Then the ANC method based on SFLA can adapt to the change of the secondary path of the system and ensure the noise reduction effect.

尽管上面已经示出和描述了本发明的实施例,可以理解的是,上述实施例是示例性的,不能理解为对本发明的限制,本领域的普通技术人员在不脱离本发明的原理和宗旨的情况下在本发明的范围内可以对上述实施例进行变化、修改、替换和变型。Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and those of ordinary skill in the art will not depart from the principles and spirit of the present invention Variations, modifications, substitutions, and alterations to the above-described embodiments are possible within the scope of the present invention without departing from the scope 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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