CN112989700B - Active noise reduction optimization method and system based on artificial immunity algorithm - Google Patents
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Abstract
The invention relates to the technical field of active noise control, in particular to an active noise reduction optimization method and system based on an artificial immunity algorithm, wherein the method comprises the following steps: s100: initializing the position of the antibody as a filter coefficient vector; s200: determining an expression of an fitness function; s300: calculating fitness function values of all antibodies; s400: calculating individual concentrations of all antibodies and calculating the excitation degree in combination with the fitness of the antibodies; s500: performing immune selection according to the excitation degree of the antibody; s600: cloning, mutating and cloning inhibition operation is carried out on population individuals subjected to immune selection to form an immune population; s700: combining the immune population and the newly generated population, and refreshing the population. According to the active noise reduction optimization method and system based on the artificial immune algorithm, provided by the invention, the diversity mechanism ensures the global optimizing capability of the algorithm, simultaneously, the mutation operator and the population refreshing mechanism are innovatively improved, the convergence is accelerated, and the performance of active noise control can be effectively improved.
Description
Technical Field
The invention relates to the technical field of active noise control, in particular to an active noise reduction optimization method and system based on an artificial immune algorithm.
Background
Along with the continuous development of industrialization, noise pollution gradually becomes an important problem affecting people's life, and the noise problem treatment can be divided into two main categories at present: active noise reduction and passive noise reduction. The passive noise reduction has weaker processing capability on low-frequency noise, and the active noise reduction can effectively inhibit the low-frequency noise by adopting a destructive interference means. With the increasing complexity of theoretical and technical solutions to various problems, the trend of ANC is to go to a wider variety of algorithms to suppress complex noise patterns, including solving three-dimensional space and time-varying signals, etc. Guide (1988, 1991) has described an increasing interest in it very early and there are many studies on ANC solutions on the market.
The inspiration of immune algorithms (immunealgorithm) comes from the principle of clonal selection used by the human immune system. In this process, when antigen enters the body, B and T lymphocytes can clone and bind to it after recognition and undergo multiple rounds of somatic hypermutation. The higher the adaptation of B cells to the existing antigen, the greater the chance of cloning, eventually destroying the antigen. The ideas of antibody immune flow of organism and optimization algorithm are well fit, and cloning and mutation processes provide diversity for algorithm, so that local optimum trapping can be effectively avoided. The application of immune algorithms to ANC would be a good direction, and no research has been done to this application.
Disclosure of Invention
The invention provides an active noise reduction optimization method and system based on an artificial immune algorithm, which have the effects of global optimization and rapid convergence and can effectively improve the performance of active noise control.
In order to solve the technical problems, the application provides the following technical scheme:
an active noise reduction optimization method and system based on an artificial immunity algorithm comprises the following steps:
s100: initializing the position of the antibody as a filter coefficient vector;
s200: determining an expression of an fitness function according to the active noise control system;
s300: calculating fitness function values of all antibodies;
s400: calculating individual concentrations of all antibodies according to the similarity between the antibodies, and calculating the excitation degree in combination with the fitness of the antibodies;
s500: performing immune selection according to the excitation degree of the antibody, and screening out the antibody with low excitation degree;
s600: cloning, mutating and cloning inhibition operation is carried out on population individuals subjected to immune selection to form an immune population;
s700: combining the immune population and the newly generated population, and refreshing the population;
s800: when the iteration condition is satisfied, the position of the optimal antibody is used as an optimal filter coefficient vector, otherwise, S400 to S800 are cyclically executed.
Further, the individual concentration of the ith antibody in S400 was calculated according to the following formula:
wherein,represents the individual concentration, N represents the number of antibodies of the population, < ->Representing antibody->And->The similarity between the two is obtained by the following formula:
wherein,for Euclidean distance or Hamming distance between antibodies,>is a similarity threshold.
Further, the degree of antibody excitation in S400 was calculated according to the following formula:
wherein,indicating the excitation degree->The fitness of the ith antibody is shown, and a and b are excitation coefficients.
Further, the S500 includes:
s500-1: according to the order of the excitation degree, the antibodies with preset proportion are selected for immune selection from small to large, and the rest antibody individuals are discarded.
Further, the S600 includes:
s600-1: copying M parts of all individuals of the immune population, and reserving clone source individuals;
s600-2: the following mutation procedure was performed on M-1 individuals:
wherein,is->The j-th dimension component of the mth replicator of the antibody, rand is a random number between 0 and 1,/o>For mutation probability->For a defined neighborhood range>Is->A unit direction vector toward the j-th dimension component of the optimal antibody;
s600-3: and calculating the excitation degree of M parts of cloned variant antibodies corresponding to each antibody in the immune population, and retaining the antibody with the minimum excitation degree to form a new immune population.
Further, the S700 includes:
s700-1: randomizing to generate a new population with the size of the number of the antibodies discarded in the immune selection before generation, and calculating the population excitation degree;
s700-2: combining the immune population with the new immune population to obtain the optimal antibody with minimum excitation degree.
Further, the immune selection proportion of the immune population is 10% -50% of the size of the antibody population; when the immune population is subjected to clone variation, the amplification factor is 5-10 times.
Furthermore, the application also discloses an active noise reduction optimization system based on the artificial immune algorithm, and the active noise reduction optimization method based on the artificial immune algorithm is used.
The beneficial effects of the invention are as follows:
in the technical scheme, an artificial immune algorithm is combined with active noise reduction, and the optimal filter coefficient vector is obtained continuously and iteratively through immune selection, cloning and mutation operation of the antibody, so that the global optimizing capability of the algorithm is ensured by a diversity mechanism. Meanwhile, aiming at the field of the invention, a mutation operator and a population refreshing mechanism are innovatively improved, the convergence of the mutation operator and the population refreshing mechanism is quickened, and the performance of active noise control can be effectively improved.
Drawings
FIG. 1 is a flowchart of an algorithm in an embodiment of an active noise reduction optimization method and system based on an artificial immune algorithm in the present invention;
FIG. 2 is a diagram of a feedforward active noise control system in an embodiment of an active noise reduction optimization method and system based on an artificial immune algorithm in the present invention;
fig. 3 is a diagram of a feedback active noise control system in an embodiment of an active noise reduction optimization method and system based on an artificial immune algorithm in the present invention.
Detailed Description
The following is a further detailed description of the embodiments:
the active noise reduction optimization system based on the artificial immunity algorithm of the embodiment uses an active noise reduction optimization method based on the artificial immunity algorithm. The active noise reduction optimizing system based on the artificial immunity algorithm comprises a feedforward noise reduction part and a feedback noise reduction part, wherein the feedforward noise reduction part is shown in fig. 2, a noise signal generated by a noise source is x (n), one path passes through a primary channel P (z), and the other path passes through a secondary channel G (z) and a feedforward filter W (z). The value of W (z) is controlled by IA algorithm so that the output error signal e (n) is as close to 0 as possible, i.e. the two signals are superimposed to produce destructive interference, thereby realizing feedforward noise reduction.
The feedback noise reduction part is shown in fig. 3, and the noise signal generated by the noise source is x (n) which passes through the primary channel P (z) and is overlapped with another signal to obtain an error signal e (n). Meanwhile, the error signal is overlapped with the noise signal after passing through the primary channel P (z) through feedback and passing through the secondary channel G (z) and the feedforward filter W (z), and the value of the W (z) is controlled through an IA algorithm, so that the output error signal e (n) is as close to 0 as possible, that is, the two signals are overlapped to generate destructive interference, and feedback noise reduction is realized.
As shown in fig. 1, the active noise reduction optimization method based on the artificial immune algorithm of the embodiment includes the following steps:
s1: an active noise control system is established based on an active noise reduction principle; in this embodiment, S1 specifically includes:
s1-1: a feedforward active noise control system is established, and the feedforward active noise control system comprises a primary channel response P (z), a secondary channel response G (z) and a filter response W (z), wherein a noise signal x (n) respectively passes through the primary channel and the secondary channel added with the filter to synthesize an error signal e (n) as a fitness function of an artificial immunity algorithm IA. The primary channel response P (z) represents the acoustic transfer equation from the source of noise to the human ear (when the earphone is worn), and the secondary channel response G (z) refers to the acoustic transfer equation from the earphone horn to the human ear.
S1-2: a feedback active noise control system is established, and comprises a primary channel response P (z), a secondary channel response G (z) and a filter response W (z), wherein a synthesized error signal e (n) is used as a fitness function of an artificial immunity algorithm IA and is also used as a feedback signal, and after the feedback signal passes through the secondary channel of the filter, a new error signal is synthesized with a noise signal passing through the primary channel.
S2: determining an adaptability function expression of an artificial immunity algorithm according to a transfer function of the active noise control system and a mean square value of an error signal; specifically, in this embodiment, the mean square value of the error signal is:
where N represents the number of sampling points, in this embodiment 200,representing the mean square value of the error for the kth sample.
S3: establishing an artificial immune algorithm model of the active noise control system; iterative calculation is carried out to obtain the position of the antibody with the minimum excitation, namely the optimized filter coefficient;
s3 specifically comprises:
s100: initializing the positions of M antibodies, 500 in this embodiment, as a filter coefficient vector;
s200: determining an expression of an fitness function according to the active noise control system;
s300: the fitness function values of all antibodies are calculated, wherein the representation of the error signal in the frequency domain is:
where X (z) is a representation of the noise signal in the frequency domain.
S400: calculating individual concentrations of all antibodies according to the similarity between the antibodies, and calculating the excitation degree in combination with the fitness of the antibodies; individual concentration of ith antibodyThe calculation is as follows:
wherein N represents the number of antibodies in the population,representing antibody->And->The similarity between the two is obtained by the following formula:
wherein, in the present embodiment,for Euclidean distance between antibodies, +.>The similarity threshold is 2.5.
Degree of excitation of the antibodyThe calculation is as follows:
wherein,fitness for the ith antibodyA and b are excitation coefficients, 1.0 and 0.5 in this embodiment, respectively.
S500: performing immune selection according to the excitation degree of the antibody, and screening out the antibody with low excitation degree; the S500 includes:
s500-1: according to the order of the excitation degree, the antibodies with preset proportion are selected for immune selection from small to large, and the rest antibody individuals are discarded. The immune selection proportion of the immune population is 10% -50% of the size of the antibody population; in this embodiment, 50%; when the immune population is subjected to cloning variation, the amplification factor is 5-10 times, in this embodiment 10 times.
S600: cloning, mutating and cloning inhibition operation is carried out on population individuals subjected to immune selection to form an immune population;
s600 specifically includes:
s600-1: copying M parts of all individuals of the immune population, and reserving clone source individuals;
s600-2: the following mutation procedure was performed on M-1 individuals:
wherein,is->The j-th dimension component of the mth replicator of the antibody, rand is a random number between 0 and 1,/o>For mutation probability->For a defined neighborhood range>Is->The first to the optimal antibodyA unit direction vector of the j-dimensional component;
s600-3: and calculating the excitation degree of M parts of cloned variant antibodies corresponding to each antibody in the immune population, and retaining the antibody with the minimum excitation degree to form a new immune population.
S700: combining the immune population and the newly generated population, and refreshing the population; the method specifically comprises the following steps:
s700-1: randomizing to generate a new population with the size of the number of the antibodies discarded in the immune selection before generation, and calculating the population excitation degree;
s700-2: combining the immune population with the new immune population to obtain the optimal antibody with minimum excitation degree.
S800: when the iteration condition is satisfied, the position of the optimal antibody is used as an optimal filter coefficient vector, otherwise, S400 to S800 are cyclically executed.
The foregoing is merely an embodiment of the present invention, the present invention is not limited to the field of this embodiment, and the specific structures and features well known in the schemes are not described in any way herein, so that those skilled in the art will know all the prior art in the field before the application date or priority date, and will have the capability of applying the conventional experimental means before the date, and those skilled in the art may, in light of the teaching of this application, complete and implement this scheme in combination with their own capabilities, and some typical known structures or known methods should not be an obstacle for those skilled in the art to practice this application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (6)
1. The active noise reduction optimization method based on the artificial immunity algorithm is characterized by comprising the following steps of: the method comprises the following steps:
s100: initializing the position of the antibody as a filter coefficient vector;
s200: determining an expression of an fitness function according to the active noise control system;
s300: calculating fitness function values of all antibodies;
s400: calculating individual concentrations of all antibodies according to the similarity between the antibodies, and calculating the excitation degree in combination with the fitness of the antibodies;
s500: performing immune selection according to the excitation degree of the antibody, and screening out the antibody with low excitation degree;
s600: cloning, mutating and cloning inhibition operation is carried out on population individuals subjected to immune selection to form an immune population;
s700: combining the immune population and the newly generated population, and refreshing the population;
s800: when the iteration condition is satisfied, the position of the optimal antibody is used as an optimal filter coefficient vector, otherwise, S400 to S800 are circularly executed;
the individual concentration of the ith antibody in S400 was calculated according to the following formula:
wherein,represents the individual concentration, N represents the number of antibodies of the population, < ->Representing antibody->And->The similarity between the two is obtained by the following formula:
wherein,for Euclidean distance or Hamming distance between antibodies,>is a similarity threshold;
the degree of antibody excitation in S400 was calculated according to the following formula:
wherein,indicating the excitation degree->The fitness of the ith antibody is shown, and a and b are excitation coefficients.
2. The active noise reduction optimization method based on the artificial immunity algorithm according to claim 1, wherein: the S500 includes:
s500-1: according to the order of the excitation degree, the antibodies with preset proportion are selected for immune selection from small to large, and the rest antibody individuals are discarded.
3. The active noise reduction optimization method based on the artificial immunity algorithm according to claim 2, wherein: the S600 includes:
s600-1: copying M parts of all individuals of the immune population, and reserving clone source individuals;
s600-2: the following mutation procedure was performed on M-1 individuals:
wherein,is->The j-th dimension component of the mth replicator of the antibody, rand is a random number between 0 and 1,/o>For mutation probability->For a defined neighborhood range>Is->A unit direction vector toward the j-th dimension component of the optimal antibody;
s600-3: and calculating the excitation degree of M parts of cloned variant antibodies corresponding to each antibody in the immune population, and retaining the antibody with the minimum excitation degree to form a new immune population.
4. The active noise reduction optimization method based on the artificial immunity algorithm according to claim 3, wherein: the S700 includes:
s700-1: randomizing to generate a new population with the size of the number of the antibodies discarded in the immune selection before generation, and calculating the population excitation degree;
s700-2: combining the immune population with the new immune population to obtain the optimal antibody with minimum excitation degree.
5. The active noise reduction optimization method based on the artificial immunity algorithm according to claim 4, wherein: the immune selection proportion of the immune population is 10% -50% of the size of the antibody population; when the immune population is subjected to clone variation, the amplification factor is 5-10 times.
6. An active noise reduction optimization system based on an artificial immunity algorithm is characterized in that: active noise reduction optimization method based on artificial immune algorithm according to any one of claims 1-5 is used.
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| CN101968853A (en) * | 2010-10-15 | 2011-02-09 | 吉林大学 | Improved immune algorithm based expression recognition method for optimizing support vector machine parameters |
| CN105510066A (en) * | 2015-11-25 | 2016-04-20 | 长兴昇阳科技有限公司 | Adaptive-noise-reduction-algorithm-based same-class rotary machinery system fault diagnosis method |
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| CN101968853A (en) * | 2010-10-15 | 2011-02-09 | 吉林大学 | Improved immune algorithm based expression recognition method for optimizing support vector machine parameters |
| CN105510066A (en) * | 2015-11-25 | 2016-04-20 | 长兴昇阳科技有限公司 | Adaptive-noise-reduction-algorithm-based same-class rotary machinery system fault diagnosis method |
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| Active Noise Control by an Immune Algorithm：Adaptation in Immune System as an Evolution;Yoshiteru ISHIDA et al.;IEEE;19961231;150-153 * |
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