CN113515048A - Method for establishing fuzzy self-adaptive PSO-ELM sound quality prediction model - Google Patents
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Abstract
The invention discloses a method for establishing a fuzzy self-adaptive PSO-ELM sound quality prediction model, which comprises the following steps: constructing an individual ELM prediction model through an ELM neural network; and constructing a fuzzy adaptive PSO-ELM prediction model through fuzzy control and a PSO algorithm. The method has the function of neural network regression prediction of the ELM extreme learning machine, can predict the subjective parameters of the sound quality according to the objective parameters of the sound quality, and has higher accuracy; the method has the PSO particle swarm optimization function, and can automatically search the optimal extreme learning machine parameters, so that the accuracy of model prediction is improved; the method has the function of fuzzy control self-adaptive adjustment of inertia factors of the particle swarm algorithm, and can effectively improve the convergence speed of the algorithm and improve the efficiency of the algorithm.
Description
Technical Field
The invention relates to the technical field of sound quality prediction models, in particular to a method for establishing a fuzzy self-adaptive PSO-ELM sound quality prediction model.
Background
With the application of various noise control technologies, the sound pressure level of noise in the vehicle is improved to a certain extent. However, studies have shown that the sound pressure level does not completely reflect the subjective perception of noise, and sometimes sounds with a high sound pressure level sound more pleasant than sounds with a low sound pressure level, for example, 80dB music is more comfortable than 70dB engine noise, and is less likely to cause a psychological reaction of dysphoric fatigue. Based on this phenomenon, researchers have put forward concepts of sound quality in view of the auditory characteristics of the human ears and human psychology.
In the existing sound quality evaluation, the subjective feeling of a person on sound is taken as a judgment standard, and a subjective listening test is carried out by an organization panel by means of a psychoacoustic research method to obtain a sound quality subjective evaluation result of noise. However, subjective evaluation tests have the disadvantages of poor consistency and low repeatability, and a large amount of cost is usually consumed to obtain reliable and statistically significant results, but the results are most intuitive. The objective parameter of the sound quality can be obtained by calculating parameters such as frequency and sound pressure of the sound signal. Therefore, how to efficiently predict the subjective parameters of the sound quality is very important.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above and/or other problems with the existing fuzzy adaptive PSO-ELM acoustic quality prediction model building methods.
Therefore, the problem to be solved by the present invention is how to provide a method for establishing a fuzzy adaptive PSO-ELM sound quality prediction model.
In order to solve the technical problems, the invention provides the following technical scheme: a fuzzy self-adaptive PSO-ELM sound quality prediction model establishing method comprises the steps of establishing an individual ELM prediction model through an ELM neural network; and constructing a fuzzy adaptive PSO-ELM prediction model through fuzzy control and a PSO algorithm.
As a preferable scheme of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model, the method comprises the following steps: the method comprises the following steps of constructing an independent ELM prediction model, collecting acoustic signals through acoustic signal detection equipment, wherein the acoustic signals comprise a training set, a testing set and a verification set, processing and calculating the collected signals to obtain objective acoustic quality parameters of the collected acoustic signals, and subjectively evaluating the collected acoustic signals through an organization panel to obtain subjective parameters of the collected acoustic signals; and generating an input matrix X from the acoustic quality objective parameters of the training set, generating an output matrix T from the subjective parameters of the training set, and randomly generating an input layer weight matrix W and a hidden layer threshold value matrix B.
As a preferable scheme of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model, the method comprises the following steps: obtaining a hidden layer output matrix H by using the input matrix X, the output matrix T, the input layer weight matrix W and the hidden layer threshold matrix B;
wherein h is a sigmoid function;
solving an output layer weight matrix beta by using H beta as T;
and (4) completing the construction of the basic structure of the ELM model through an ELM neural network.
As a preferable scheme of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model, the method comprises the following steps: the construction of the fuzzy adaptive PSO-ELM predictive model includes the steps of,
the PSO algorithm initially randomly generates n groups of particles according to the population scale n, each group of particles forms an input layer weight matrix W and a hidden layer threshold matrix B in the ELM neural network, training set data is input, and each group generates an ELM prediction model;
substituting the test set data into the generated ELM prediction model, solving a predicted value of the annoyance degree of the subjective parameter of the sound quality, comparing the predicted value with the annoyance degree in the test set, and taking the root mean square value of the two as a fitness value to return to a PSO algorithm;
taking the group of particles with the minimum root mean square error from the PSO algorithm as an individual extreme value, comparing the individual extreme value with the group extreme value, and replacing the group extreme value with the group of particles if the individual extreme value is smaller;
generating a new inertia factor w by the PSO algorithm according to the fitness value and the change rate thereof, updating particles according to a formula, generating n groups of new particles with the same scale, and repeating the steps;
and when the iteration reaches the upper limit of the iteration times, stopping the iteration, taking a group of particles of the group extremum to form an input layer weight matrix W and a hidden layer threshold value matrix B in the ELM neural network, and generating a final ELM prediction model by matching with training set data, wherein the model is a fuzzy self-adaptive PSO-ELM prediction model.
As a preferable scheme of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model, the method comprises the following steps: and replacing the verification set data into a fuzzy self-adaptive PSO-ELM prediction model, solving a prediction value of the subjective parameter annoyance degree of the sound quality, comparing the prediction value with the annoyance degree in the verification set, and evaluating the quality of the prediction model by taking the root mean square value of the two.
As a preferable scheme of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model, the method comprises the following steps: the objective parameters of sound quality include loudness, roughness, fluctuation, sharpness, tonality, semantic clarity and A sound pressure level.
As a preferable scheme of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model, the method comprises the following steps: and optimizing an input layer weight matrix W and a threshold matrix B of the extreme learning machine by adopting a particle swarm optimization algorithm, taking the input layer weight matrix W and the threshold matrix B of the extreme learning machine as particles of the particle swarm optimization algorithm, and substituting the obtained error root mean square value into the test set as a fitness function to perform global optimization.
As a preferable scheme of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model, the method comprises the following steps: in the step of generating a new inertia factor w by the PSO algorithm according to the population extremum adaptability value and the change rate thereof, the population extremum adaptability value and the change thereof are used as input variables, and the inertia factor w is updated by a fuzzy control method.
As a preferable scheme of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model, the method comprises the following steps: before the collected acoustic signals are subjected to data input, normalization processing is performed.
As a preferable scheme of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model, the method comprises the following steps: the acoustic signal data is normalized as follows:
the sound quality objective parameter is converted to [0,1] by the following formula,
x＝(Xi-Xmin)/(Xmax-Xmin) (3)
in the formula XiIs an objective parameter value, X, of a certain sound qualitymaxFor the corresponding maximum value, X, of the objective parameterminThe objective parameter is corresponding to the minimum value.
The method has the advantages that the method has the function of neural network regression prediction of the ELM extreme learning machine, can predict the subjective parameters of the sound quality according to the objective parameters of the sound quality, and has higher accuracy; the method has the PSO particle swarm optimization function, and can automatically search the optimal extreme learning machine parameters, so that the accuracy of model prediction is improved; the method has the function of fuzzy control self-adaptive adjustment of inertia factors of the particle swarm algorithm, and can effectively improve the convergence speed of the algorithm and improve the efficiency of the algorithm.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is an ELM neural network schematic diagram of a method for establishing a fuzzy adaptive PSO-ELM acoustic quality prediction model.
FIG. 2 is a diagram of an ELM neural network prediction model construction process of a fuzzy adaptive PSO-ELM acoustic quality prediction model construction method.
FIG. 3 is a flow chart of a particle swarm optimization algorithm of a method for establishing a fuzzy adaptive PSO-ELM acoustic quality prediction model.
FIG. 4 is a fuzzy control flow chart of a method for establishing a fuzzy adaptive PSO-ELM acoustic quality prediction model.
FIG. 5 is a diagram of a triangular membership function as a population extremum fitness value in a method for establishing a fuzzy adaptive PSO-ELM acoustic quality prediction model.
FIG. 6 is a membership function graph of delta in change of the population extremum fitness value of the method for establishing the fuzzy adaptive PSO-ELM acoustic quality prediction model.
FIG. 7 is a control rule image of a method for building a fuzzy adaptive PSO-ELM acoustic quality prediction model.
Fig. 8 is a flow chart of a fuzzy adaptive PSO algorithm of a method for establishing a fuzzy adaptive PSO-ELM acoustic quality prediction model.
Fig. 9 is a diagram of constructing an ELM prediction model in the method of establishing a fuzzy adaptive PSO-ELM acoustic quality prediction model.
FIG. 10 is a diagram of a PSO-ELM prediction model construction process of a fuzzy adaptive PSO-ELM acoustic quality prediction model construction method.
Fig. 11 is a construction diagram of a fuzzy adaptive PSO-ELM prediction model of a method for establishing a fuzzy adaptive PSO-ELM acoustic quality prediction model.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 to 11, a first embodiment of the present invention provides a method for building a fuzzy adaptive PSO-ELM acoustic quality prediction model, where the method for building the fuzzy adaptive PSO-ELM acoustic quality prediction model includes the following steps:
s1, constructing an individual ELM prediction model through an ELM neural network;
s2, constructing a fuzzy self-adaptive PSO-ELM prediction model through fuzzy control and a PSO algorithm;
an Extreme Learning Machine (ELM) is a typical single hidden layer feedforward neural network and consists of an input layer, a hidden layer and an output layer. The extreme learning machine can randomly generate an initial input weight matrix W and a hidden layer threshold matrix B, only the number of hidden layer neurons is set, and an output weight matrix is obtained by solving an equation set, so that a complete ELM neural network model is constructed, as shown in FIG. 1.
Inputting a matrix X:
in the formula xiIs the input sample.
Input weight matrix W:
in the formula wiThe weights of the ith hidden layer neuron and the input layer are obtained.
Hidden layer threshold matrix B:
in the formula biIs the threshold for the ith neuron.
The known output matrix T:
T＝[t1 tQ … tQ]'Q×m
wherein t isjFor the output of the jth output layer neuron:
in the formula, H (X) is an activation function, and a hidden layer output matrix H can be obtained by calculation through an input matrix X, an input weight matrix W and a hidden layer threshold matrix B:
output weight matrix β:
in the formula betaiThe weight of the ith neuron and the output layer.
The ELM neural network randomly generates an input weight matrix W and a hidden layer threshold matrix B, the input matrix X and a known output matrix T are used as the input conditions of a known training set, and the ELM can approach the training set without error, so that an output weight matrix beta can be obtained by reverse extrapolation:
Hβ＝T
c is a penalty coefficient, and finally, an output function expression of the ELM can also be obtained:
so far, the construction of the ELM neural network is completed.
In the prediction process by using the ELM neural network, an output matrix Y to be solved is as follows:
in the formula yiIs the output sample to be found. The hidden layer output matrix H can be obtained by calculation through the input matrix X, the input weight matrix W and the hidden layer threshold matrix B, and then the output matrix Y to be solved is obtained by solving:
Hβ＝Y
the Particle Swarm Optimization (PSO) is a swarm intelligent optimization algorithm except ant colony algorithm and fish colony algorithm in the field of intelligent computing, and the inspiration of the PSO comes from the research on bird predation.
The particle swarm algorithm firstly randomly generates a group of particles in a feasible solution, each particle represents a potential optimal solution of an optimization problem, and the characteristics of a single particle are described by three indexes of position, speed and fitness value.
The position and the speed of the particles in the solution space are continuously updated, and the movement in the solution space is further realized. Updating the individual positions by tracking the individual extremum Pbest and the group extremum Gbest (the individual extremum Pbest refers to the position with the optimal fitness value calculated in all the positions where a single particle passes, and the group extremum Gbest refers to the position with the optimal fitness value searched by all the particles in the population), calculating the fitness value once when the particles update the positions once, and updating the sizes and the positions of the individual extremum and the group extremum by comparing the fitness value of the new particles with the sizes of the individual extremum and the group extremum. The particle position update and velocity update formulas are as follows:
the particle swarm optimization algorithm flow chart is shown in fig. 3.
Fuzzy control is a nonlinear control method, which is a control method using the basic ideas and theories of fuzzy mathematics. For a complex system, the dynamic characteristics of the system are difficult to accurately describe due to too many variables, and fuzzy control is a good solution.
The fuzzy control comprises five parts of variable definition, fuzzification processing, rule table establishment, logical reasoning and output defuzzification, and the flow of the fuzzy control constructed by the method is shown in figure 4.
In the fuzzy control, a group extreme value Gbest adaptability value and the delta of the change of the group extreme value Gbest adaptability value are selected as a first input quantity and a second input quantity of a fuzzy controller, and the discourse domain of the group extreme value Gbest adaptability value is [0, 0.015 ]]The domain of discourse of delta of change of group extreme value Gbest fitness value is [0, 0.009]. And outputting the fuzzy control after defuzzification of the output of the fuzzy controller to obtain an inertia factor w of the particle swarm optimization algorithm. The variable fuzzification processing is to fuzzify the group extreme value Gtest fitness value and the change delta of the group extreme value Gtest fitness value as input quantity respectively. And taking three fuzzy subsets of the group extreme value Gbest fitness value. The ranges of the fuzzy subsets of the input group extreme value Gbest fitness value are respectively selected as follows: i is11(Small positive PS) is [ -0.075, 0, 0.0075]，I12(center PM) is [0, 0.0075, 0.015%]，I13(Positive large PB) is [0.0075, 0.015, 0.0225 ]]. Similarly, each range of the fuzzy subset corresponding to the variation delta of the group extreme value Gbest fitness value is respectively selected as follows: i is21(Positive small PS) is [ -0.0045, 0, 0.0045]，I22(median PM) is [0.003, 0.0045, 0.006 ]]，I23(Positive PB) is [0.006, 0.0075, 0.009]. And the outputs corresponding to the four fuzzy subsets of the output inertia factor w are defined as [0.2, 0.8 ]]Respectively is as follows: o is11(Positive large PB) is [0.8, 0.65, 0.5 ]]、O12(median PM) is [0.65, 0.5, 0.35 ]]、O13(Positive small PS) is [0.5, 0.35, 0.2 ]]And obtaining the corresponding inertia factor w. And then determining a logic judgment rule, and selecting a triangular membership function as a membership function of the group extremum Gtest fitness value and the delta of the change of the group extremum Gtest fitness value, wherein images of the functions are respectively shown in FIGS. 5 and 6.
In step S1, constructing the individual ELM prediction models includes the steps of,
s11: acquiring acoustic signals by acoustic signal detection equipment, wherein the acoustic signals comprise a training set, a testing set and a verification set, processing and calculating the acquired signals to obtain objective parameters of the acoustic quality of the acquired acoustic signals, and subjectively evaluating the acquired acoustic signals by an organization review group to obtain subjective parameters of the acquired acoustic signals;
s12: preprocessing, i.e. normalizing, the acquired acoustic signals before inputting the data into the acoustic quality prediction model
S13: generating an input matrix X from the acoustic quality objective parameters of the training set, generating an output matrix T from the subjective parameters of the training set, randomly generating an input layer weight matrix W and a hidden layer threshold matrix B, and optimizing the input layer weight matrix W and the threshold matrix B of the extreme learning machine by adopting a particle swarm optimization algorithm. And taking an input layer weight matrix W and a threshold matrix B of the extreme learning machine as particles of the particle swarm optimization algorithm, and taking an error root mean square value as a fitness function to carry out global optimization.
S14: obtaining a hidden layer output matrix H by using the input matrix X, the output matrix T, the input layer weight matrix W and the hidden layer threshold matrix B;
wherein h is a sigmoid function;
solving an output layer weight matrix beta by using H beta as T;
and (4) completing the construction of the basic structure of the ELM model through an ELM neural network.
In step S11, the objective parameters of sound quality include loudness, roughness, waviness, sharpness, tonality, semantic clarity, and a sound pressure level; wherein,
(1) loudness
Loudness is the sound of the human earPsychoacoustic index of total loudness of sound is Song (tone), and defines the loudness of pure tone with frequency of 1000Hz and sound pressure level of 40dB as 1 tone. And the loudness level of a sound is defined as the sound pressure level equal to a 1000Hz pure tone, where L is the loudness levelNExpressed in units of square (phon).
The international standard ISO532 specifies a loudness calculation method, which comprises two methods of Stevens and Zwicker, and the ISO532B Zwicker method is suitable for both a diffuse sound field and a free sound field, so that the method is generally selected to calculate the loudness in the research of the sound quality of automobiles. The process is as follows:
determining external ear and middle ear transfer functions;
filtering by using an auditory filter to obtain an excitation level E of each critical frequency band;
calculating the characteristic loudness N' of each critical frequency band according to the obtained excitation level:
in the formula, ETQFor stimulation under the auditory threshold, E0Excitation corresponding to reference sound pressure;
and IV, integrating the specific loudness N' in a Bark domain to obtain the total loudness N:
(2) degree of fluctuation
When two sound signals with different frequencies and different amplitudes are superposed together, a modulation effect is generated, the fluctuation describes that when the modulation frequency is 0.5 to 20Hz, the subjective feeling of a person on slowly modulated sound is a physical quantity reflecting the fluctuation of the sound brightness degree, the unit is vacil, and the fluctuation of the 60dB 1kHz pure sound after being modulated by the frequency of 4Hz and the amplitude of 100 percent is defined as 1 vacil. Calculating a model by adopting Zwicker:
in the formula (f)modIs the modulation frequency; Δ LEFor masking depth, it is positively correlated with the amount of change in sound.
(3) Roughness of
Roughness is a psychological index describing the human subjective feeling of rapidly modulated sound when the modulation frequency is 20 to 300Hz, and is a feeling of noise, harshness, etc., reflecting the sound, and is expressed in unit of asper. The roughness of a 60dB 1kHz pure tone after modulation with 70Hz frequency and 100% amplitude is defined as 1 asper. Using an Aures roughness calculation model:
in the formula fmodIs the modulation frequency; Δ LEFor masking depth, which is positively correlated with the amount of sound variation, masking depth Δ LEAn increase in roughness results.
(4) Sharpness degree
Sharpness is a psychoacoustic indicator describing the high frequency content of sound, in acum. Defined within a 150Hz bandwidth with a center frequency of 1000Hz, a narrow-band noise of 60dB is defined as 1 accum. The calculating method adopts a Zwicker sharpness calculating model:
wherein k is a weighting coefficient, and is generally 0.11; n is the overall loudness; n' (z) is the specific loudness of the Bark field, number z; g (z) is a function of the weight coefficients in the different Bark domains:
(5) tone scheduling
Tone scheduling, also known as pure tone, describes the degree of prominence of a pure tone in a sound, which reflects the pure tone heard by a human or a sound within a bandwidth less than a critical frequency band, in tu. A1 kHz pure tone of 60dB is defined as 1 tu. Tonality is generally calculated using the method proposed by Terhardt and Aures:
in the formula, W1(Δzi) Is the difference of critical bands of the ith single-frequency component domain; w2(fi) Is the relationship of frequency to the ith single frequency component; w3(ΔLi) Is the sound level surplus effect of the ith single-frequency component.
(6) Semantic clarity
Speech intelligibility is an index describing the intelligibility of speech in noisy environments, expressed in percentage terms. Studies have shown that when the noise is 12dB above the pitch of the speaking voice, the speaking voice is completely inaudible, i.e. AI is 0%, and then an upper noise limit ul (f) can be determined; when the noise is 30dB below the upper noise limit, the speaking voice is completely heard clearly, i.e. AI equals 100%, and a lower noise limit ll (f) is determined. Thus, there is the following equation:
UL(f)＝H(f)+12
LL(f)＝UL(f)-30
where H (f) is the sound pressure level of the speaking voice.
Because the frequency of the daily speaking voice of people is basically within the range of 200-6000 Hz, a weighting coefficient W (f) is introduced to weight different frequencies, and the value of W (f) is the maximum in the middle frequency band. The weighting coefficient is used together with noise to calculate the speech intelligibility AI, and the calculation formula is as follows:
AI＝∑W(f)D(f)/30
in step S12, the acoustic signal data is normalized as follows:
the sound quality objective parameter is converted to [0,1] by the following formula,
x＝(Xi-Xmin)/(Xmax-Xmin) (3)
in the formula XiIs an objective parameter value, X, of a certain sound qualitymaxFor the corresponding maximum value, X, of the objective parameterminThe objective parameter is corresponding to the minimum value.
In step S2, constructing the fuzzy adaptive PSO-ELM predictive model includes the steps of,
s21: the PSO algorithm initially randomly generates n groups of particles according to the population scale n, each group of particles forms an input layer weight matrix W and a hidden layer threshold matrix B in the ELM neural network, training set data is input, and each group generates an ELM prediction model;
s22: substituting the test set data into the generated ELM prediction model, solving a predicted value of the annoyance degree of the subjective parameter of the sound quality, comparing the predicted value with the annoyance degree in the test set, and taking the root mean square value of the two as a fitness value to return to a PSO algorithm;
s23: taking the group of particles with the minimum root mean square error from the PSO algorithm as an individual extreme value, comparing the individual extreme value with the group extreme value, and replacing the group extreme value with the group of particles if the individual extreme value is smaller;
s24: generating a new inertia factor w by the PSO algorithm according to the fitness value and the change rate thereof, updating particles according to a formula, generating n groups of new particles with the same scale, and repeating the steps;
s25: when iteration reaches the upper limit of iteration times, the iteration is stopped, a group of particles of a group extreme value are taken to form an input layer weight matrix W and a hidden layer threshold value matrix B in the ELM neural network, and a final ELM prediction model is generated by matching with training set data and is a fuzzy self-adaptive PSO-ELM prediction model;
s26: and replacing the verification set data into a fuzzy self-adaptive PSO-ELM prediction model, solving a prediction value of the subjective parameter annoyance degree of the sound quality, comparing the prediction value with the annoyance degree in the verification set, and evaluating the quality of the prediction model by taking the root mean square value of the two.
Further, in the step of generating a new inertia factor w by the PSO algorithm according to the fitness value and the change rate thereof, the fitness value and the change rate thereof are used as input variables, and the inertia factor w is updated by a fuzzy control method.
Example 2
Referring to fig. 1 to 11, a second embodiment of the present invention is based on the above embodiment.
The acquisition of acoustic signal data by an acoustic signal detection device is shown in table 1:
TABLE 1 data set collected
The data samples of groups 1-30 are selected as training set, the samples of groups 31-35 are selected as testing set, and the samples of groups 36-40 are selected as verifying set.
Inputting a matrix X:
in the formula xiIs the input sample.
Substituting the objective parameters of the training set samples as input samples, the first row data being (x)11)-(xn1) The first column data is (x)11)-(x1Q) And so on, the last row of data is (x)1Q)-(xnQ) The last column of data is (x)n1)-(xnQ)。
Table 2 training set sample data
(2) Input weight matrix W:
in the formula wiThe weights of the ith hidden layer neuron and the input layer are obtained. The parameters are randomly generated in the initialization process and are calculated as known parameters.
(3) Hidden layer threshold matrix B:
in the formula biIs the threshold for the ith neuron. The parameters are randomly generated in the initialization process and are calculated as known parameters.
(4) The known output matrix T:
T＝[t1 t2 … tQ]'Q×m
wherein t isjFor the output of the jth output layer neuron:
wherein h (x) is activation function, subjective parameters of training set samples are used as known output matrix, and the first column of data is (t)1)-(tQ)。
TABLE 3 subjective annoyance
(5) And calculating to obtain a hidden layer output matrix H by using the input matrix X, the input layer weight matrix W and the hidden layer threshold matrix B:
(6) output weight matrix β:
in the formula betaiThe weight of the ith neuron and the output layer.
The ELM neural network randomly generates an input weight matrix W and a hidden layer threshold matrix B, the input matrix X, a hidden layer output matrix H and a known output matrix T are used as the input conditions of a known training set, and the ELM can approach the training set without error, so that an output weight matrix beta can be obtained by reverse extrapolation:
Hβ＝T
at this point, the construction of the ELM prediction model is completed, as shown in FIG. 9.
The particle swarm algorithm searches a space dimension D, and the space dimension D is calculated by adopting the following formula:
D＝I*H+H
in the formula, I is the number I of input sample neurons which is 7; h is the number of neurons in the hidden layer, and H is 7;
the particle swarm initialization settings are as follows: t is tmaxIs 100 times; learning factor c1And c21.4 are taken; maximum velocity v of particlesmaxAnd minimum velocity vminAre 1 and-1, respectively; maximum position x of particlemaxAnd a minimum position xmin1 and-1 respectively.
The fitness function selects the root mean square value of the prediction output value obtained by carrying out reverse substitution on the training set samples and the actual output value samples of the training set samples as the calculation basis:
in the formula, N is the amount of training samples; m is the number of output sample neurons; y iskA predicted output value for the test set; c. CkTo test the set actual output values, the PSO-ELM predictive model is shown in FIG. 10.
Designing a fuzzy knowledge base, namely a fuzzy rule according to experience and a change rule: when the population extreme value Gbest fitness value is larger and the change delta of the population extreme value Gbest fitness value is larger, the inertia factor w is larger; when the smaller the fitness value of the group extreme value Gbest is and the smaller the change delta of the fitness value of the group extreme value Gbest is, the smaller the inertia factor w is. A fuzzy control rule is designed according to the idea that the population extreme value Gbest fitness value is rapidly converged along with the iteration number and is not easy to fall into the local minimum value, as shown in table 4. The image corresponding to the fuzzy control rule table is shown in fig. 7:
TABLE 4 fuzzy control rules Table
Let T be the sampling period, n be the number of points sampled, the input of the fuzzy controller is represented by Gbest (nT), delta (nT) and the output is represented by w (nT), after fuzzification processing, S (nT), V (nT) and F (nT) can be obtained, then the control rule can be represented as follows:
R(nT)＝S(nT)V(nT)F(nT)
defuzzification can be realized according to a logic judgment rule (TS inference), a fuzzy control rule and the corresponding size of each output fuzzy subset. Although the simple PSO algorithm can also achieve the purpose of group optimization, the convergence speed is low, the convergence can be achieved only by multiple iterations, and the calculation efficiency is low. Therefore, on the basis of the PSO algorithm, the fuzzy adaptive control is added, the adaptive change of the inertia factor w in the iterative process is realized, so as to achieve a better calculation effect, the flow chart of the fuzzy adaptive PSO algorithm is shown in FIG. 8, and the fuzzy adaptive PSO-ELM prediction model is shown in FIG. 11.
The prediction process of the built ELM prediction model is as follows:
(1) inputting a matrix X:
in the formula xiIs the input sample.
Substituting the objective parameters of the verification set samples as input samples, the first row data being (x)11)-(xn1) The first column data is (x)11)-(x1Q) And so on, the last row of data is (x)1Q)-(xnQ) The last column of data is (x)n1)-(xnQ)：
Table 5 verification set data
(6) From the known conditions: inputting a weight matrix W, a hidden layer threshold matrix B, an input matrix X and a hidden layer output matrix H to obtain an output matrix Y:
Y＝[y1 y2 … y5]'5×1
Y＝Hβ
table 6 output matrix data
This completes the prediction.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A method for establishing a fuzzy self-adaptive PSO-ELM sound quality prediction model is characterized by comprising the following steps: the method comprises the following steps:
constructing an individual ELM prediction model through an ELM neural network;
and constructing a fuzzy adaptive PSO-ELM prediction model through fuzzy control and a PSO algorithm.
2. The method of building a fuzzy adaptive PSO-ELM acoustic quality prediction model of claim 1, wherein: constructing a separate ELM predictive model includes the steps of,
acquiring acoustic signals by acoustic signal detection equipment, wherein the acoustic signals comprise a training set, a testing set and a verification set, processing and calculating the acquired signals to obtain objective parameters of the acoustic quality of the acquired acoustic signals, and subjectively evaluating the acquired acoustic signals by an organization review group to obtain subjective parameters of the acquired acoustic signals;
and generating an input matrix X from the acoustic quality objective parameters of the training set, generating an output matrix T from the subjective parameters of the training set, and randomly generating an input layer weight matrix W and a hidden layer threshold value matrix B.
3. The method of building a fuzzy adaptive PSO-ELM acoustic quality prediction model of claim 2, wherein: obtaining a hidden layer output matrix H by using an input matrix x, an output matrix T, an input layer weight matrix W and a hidden layer threshold matrix B;
wherein h is a sigmoid function;
solving an output layer weight matrix beta by using H beta as T;
and (4) completing the construction of the basic structure of the ELM model through an ELM neural network.
4. The method of constructing a fuzzy adaptive PSO-ELM acoustic quality prediction model as claimed in claim 2 or 3, wherein: the construction of the fuzzy adaptive PSO-ELM predictive model includes the steps of,
the PSO algorithm initially randomly generates n groups of particles according to the population scale n, each group of particles forms an input layer weight matrix W and a hidden layer threshold matrix B in the ELM neural network, training set data is input, and each group generates an ELM prediction model;
substituting the test set data into the generated ELM prediction model, solving a predicted value of the annoyance degree of the subjective parameter of the sound quality, comparing the predicted value with the annoyance degree in the test set, and taking the root mean square value of the two as a fitness value to return to a PSO algorithm;
taking the group of particles with the minimum root mean square error from the PSO algorithm as an individual extreme value, comparing the individual extreme value with the group extreme value, and replacing the group extreme value with the group of particles if the individual extreme value is smaller;
generating a new inertia factor w by the PSO algorithm according to the fitness value and the change rate thereof, updating particles according to a formula, generating n groups of new particles with the same scale, and repeating the steps;
and when the iteration reaches the upper limit of the iteration times, stopping the iteration, taking a group of particles of the group extremum to form an input layer weight matrix W and a hidden layer threshold value matrix B in the ELM neural network, and generating a final ELM prediction model by matching with training set data, wherein the model is a fuzzy self-adaptive PSO-ELM prediction model.
5. The method of building a fuzzy adaptive PSO-ELM acoustic quality prediction model as claimed in claim 4, wherein: and replacing the verification set data into a fuzzy self-adaptive PSO-ELM prediction model, solving a prediction value of the subjective parameter annoyance degree of the sound quality, comparing the prediction value with the annoyance degree in the verification set, and evaluating the quality of the prediction model by taking the root mean square value of the two.
6. The method for building the fuzzy adaptive PSO-ELM acoustic quality prediction model according to any one of claims 1-3 or 5, wherein: the objective parameters of sound quality include loudness, roughness, fluctuation, sharpness, tonality, semantic clarity and A sound pressure level.
7. The method of claim 6, wherein the method comprises: and optimizing an input layer weight matrix W and a threshold matrix B of the extreme learning machine by adopting a particle swarm optimization algorithm, taking the input layer weight matrix W and the threshold matrix B of the extreme learning machine as particles of the particle swarm optimization algorithm, and substituting the obtained error root mean square value into the test set as a fitness function to perform global optimization.
8. The method of building a fuzzy adaptive PSO-ELM acoustic quality prediction model as claimed in claim 4, wherein: in the step of generating a new inertia factor w by the PSO algorithm according to the fitness value and the change rate thereof, the fitness value and the change rate thereof are used as input variables, and the inertia factor w is updated by a fuzzy control method.
9. The method of constructing a fuzzy adaptive PSO-ELM acoustic quality prediction model according to claim 7 or 8, wherein: before the collected acoustic signals are subjected to data input, normalization processing is performed.
10. The method of building a fuzzy adaptive PSO-ELM acoustic quality prediction model of claim 9, wherein: the acoustic signal data is normalized as follows:
the sound quality objective parameter is converted to [0,1] by the following formula,
x＝(Xi-Xmin)/(Xmax-Xmin) (3)
in the formula XiIs an objective parameter value, X, of a certain sound qualitymaxFor the corresponding maximum value, X, of the objective parameterminThe objective parameter is corresponding to the minimum value.
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