CN117686555A - LC humidity sensor drift compensation method based on machine learning - Google Patents
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Abstract
The invention discloses a machine learning-based LC humidity sensor drift compensation method, which comprises the following steps: carrying out a temperature test experiment on the LC humidity sensor to obtain original data, wherein the original data comprises a plurality of groups of data, and each group of data comprises response data under humidity and temperature conditions; preprocessing response data, extracting response bandwidth BW, real part impedance maximum Re #) Max and the resonance frequency Freq corresponding to the maximum value thereof, denoising the Max by utilizing wavelet analysis, and carrying out normalization processing on the denoised data to be used as a characteristic to establish a data set; initializing a BP neural network, and optimizing the BP neural network model by adopting a genetic algorithm to obtain a GA-BP model; the GA-BP model is trained with the training set, and the trained model is used for compensation of the drift data test set. The invention compensates the drift of the LC humidity sensor through the neural network model, and the model is obtained through learning the drift data setThe coefficient reaches 0.966, the model can explain 96.6% of uncertainty, and good compensation effect is obtained.
Description
Technical Field
The invention relates to the technical field of LC sensors, in particular to a drift compensation method of an LC humidity sensor based on machine learning.
Background
LC passive wireless sensors are essentially LC resonant loops consisting of passive element resistance, capacitance and inductance. The LC sensor has small volume, low cost and lower power consumption, does not need to replace a battery, can be used for detecting parameters such as pH monitoring, temperature, humidity, biological potential, strain and the like, and has important use value and scientific research significance.
However, the sensor often has drift phenomenon due to external environmental changes (temperature, electromagnetic interference, etc.), that is, the sensor response deviates from the reference value, which may lead to inaccurate subsequent pattern recognition results. Aiming at the problem of unstable response data of the LC humidity sensor caused by temperature change, the neural network model is adopted to compensate the temperature drift of the LC humidity sensor, and the method has important significance in obtaining a more stable and more reliable output result.
In view of the above, it is necessary to design a method for compensating LC humidity sensor drift based on machine learning to solve the above-mentioned problems.
Disclosure of Invention
The invention aims to provide a drift compensation method of an LC humidity sensor based on machine learning, aiming at the problem of unstable response data of the LC humidity sensor caused by temperature change.
In order to achieve the above purpose, the invention adopts the following technical scheme that the method comprises the following steps:
s1, performing a temperature test experiment on an LC humidity sensor to obtain original data, wherein the original data comprises a plurality of groups of data, and each group of data comprises response data of the LC humidity sensor under a humidity environment and a temperature condition;
s2, preprocessing response data of the LC humidity sensor, extracting response bandwidth BW and real part impedance maximum value Re #) Max, and said maximum value Re (>) The method comprises the steps of (1) denoising the resonance frequency Freq corresponding to Max by utilizing wavelet analysis, and taking the denoised data as a characteristic to establish a data set after normalization processing;
s3, initializing a BP neural network, and optimizing the BP neural network model by adopting a genetic algorithm to obtain a GA-BP model;
and S4, training the GA-BP model by using a training set, and using the trained model for compensation of a drift data testing set.
As a further improvement of the present invention, the basis function used for the wavelet analysis in S2 is db10, and the number of decomposition layers is 3.
As a further improvement of the present invention, the threshold value of the wavelet analysis in S2 is selected as a modified fixed threshold value, expressed as:
，
the threshold function is selected as an improved threshold function, and the formula of the threshold function is as follows:
，
wherein the method comprises the steps ofAs the standard deviation of the signal,Nfor the length of the data to be the same,jto decompose the layer number->For estimating wavelet coefficients +.>For the decomposed wavelet coefficients sgn is a sign piecewise function.
As a further improvement of the present invention, the BP neural network in the S3 includes 1 input layer, 2 hidden layers, and 1 output layer, the input layer having 3 inputs, the 2 hidden layers having m and n neurons, respectively.
As a further improvement of the invention, the BP neural network performs nonlinear transformation through linear combination of inputs and then through a neuron activation function, wherein the neuron activation function selects a Sigmoid function to perform mean square error of an estimated value and an actual measured valueMSEA function as an error function and minimizing the error function using a back propagation algorithm, wherein the mean square errorMSEThe expression of the function is:
wherein,for the actual observations +.>The output value is predicted for the model and,Nis the number of test points.
As a further improvement of the present invention, the genetic algorithm in S3 optimizes the BP neural network, by continuously evolving individuals in the population, by selecting, crossing and mutating to explore the hyper-parameter space, to obtain an optimal hyper-parameter combination, and after multiple iterations, the genetic algorithm converges to individuals with high fitness, which correspond to the hyper-parameter combination.
As a further improvement of the present invention, the genetic algorithm uses a decision coefficient when optimizing the BP neural networkAs a model evaluation index, the determination coefficient +.>The expression of (2) is:
wherein,for the actual observations +.>For the mean value of the actual observations, +.>The output value is predicted for the model and,Nis the number of test points.
The beneficial effects of the invention are as follows:
the invention uses neural network modelCompensating for temperature drift of LC humidity sensor, and learning the temperature drift data set to obtain the modelThe coefficient reaches 0.966, which shows that the model can explain 96.6% uncertainty and obtain good effect.
Drawings
FIG. 1 is a flow chart of a method for suppressing temperature drift of an LC passive wireless sensor using a neural network model according to an embodiment of the present invention;
FIG. 2 is a feature diagram of a selection of a temperature drift dataset for establishing an LC passive wireless humidity sensor;
FIG. 3 is a graph of noise reduction for real impedance maximum data at 15℃and relative humidity varying from 50% RH to 95% RH;
FIG. 4 is a graph of noise reduction for resonant frequency data at 35℃and relative humidity varying from 50% RH to 95% RH;
FIG. 5 is a diagram of an initializing neural network of the present invention;
FIG. 6 is a graph of the comparison of the results of the temperature drift data fitting with the trained neural network model and the actual results according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
It should be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and/or processing steps closely related to aspects of the present invention are shown in the drawings, and other details not greatly related to the present invention are omitted.
As shown in fig. 1, the method for compensating the drift of the LC humidity sensor based on the neural network model according to the present invention includes:
s1, performing a temperature test experiment on an LC passive wireless humidity sensor to obtain original data, wherein the original data comprise a plurality of groups of data, and each group of data comprises response data of the LC humidity sensor under a humidity environment and a temperature condition.
S2, preprocessing LC response data, wherein FIG. 2 is a response curve of the LC humidity sensor when the temperature is 25 ℃ and the relative humidity is 60%RH, and extracting response bandwidth BW and real part impedance maximum Re #, as shown in FIG. 2) Max, and its maximum value.
The wavelet analysis is utilized to reduce noise of the extracted data, the adopted base function is db10, the number of decomposition layers is 3, the wavelet analysis threshold is selected as an improved fixed threshold, and the expression is as follows:
，
the threshold function is selected as an improved threshold function, and the formula of the threshold function is as follows:
，
wherein the method comprises the steps ofAs the standard deviation of the signal,Nfor the length of the data to be the same,jto decompose the layer number->For estimating wavelet coefficients +.>For the decomposed wavelet coefficients sgn is a sign piecewise function.
Fig. 3 is a graph of noise reduction of real impedance maximum data at 15 ℃ when the relative humidity is changed from 50% rh to 95% rh, and fig. 4 is a graph of noise reduction of resonance frequency data at 35 ℃ when the relative humidity is changed from 50% rh to 95% rh, and the data after noise reduction is normalized to create a data set as a feature.
S3, initializing BP neural network, as shown in FIG. 5, comprising 1 input layer, 2 hidden layers and 1 output layer, which inputsThere are 3 inputs to the layer and m and n neurons in the 2 hidden layers, respectively. The neural network is constructed by linear combination of inputs and nonlinear transformation of the inputs through an activation function, wherein the neuron activation function is a Sigmoid function, and the mean square error of an estimated value and an actual measured value is calculatedMSE) The function is used as an error function, and the error function is minimized by using a back propagation algorithm, wherein the expression of the mean square error is as follows:
wherein,for the actual observations +.>And predicting an output value for the model, wherein N is the number of test points.
And optimizing the BP neural network model by adopting a genetic algorithm to obtain a GA-BP model, optimizing the super parameters of the network, and searching for the combination of the optimal learning rate and the neuron number of the hidden layer.
Using the decision coefficient [ ]) As a model evaluation index, < >>The coefficients reflect the ratio of the total variation of the dependent variable to be interpreted by the independent variable by regression relation, ++>The closer the coefficient is to 1, the better the regression fit is, and the expression is:
wherein,for the actual observations +.>For the mean value of the actual observations, +.>The output value is predicted for the model and,Nis the number of test points.
The model uses an optimizer of Adam, the iteration number (epoch) is 10000, the super parameters used comprise the number of neurons of the first layer of hidden layer is 95, the number of neurons of the second layer of hidden layer is 81, and the learning rate is 0.00095.
And S4, training the GA-BP model by using a training set, and using the trained model for compensation of a drift data testing set. Figure 6 is a graph of analysis of the results of fitting the trained neural network model to temperature drift data versus the actual results,the coefficient reaches 0.966.
In summary, the invention compensates the temperature drift of the LC humidity sensor through the neural network model, and the model is obtained through the study of the temperature drift data setThe coefficient reaches 0.966, which shows that the model can explain 96.6% uncertainty and obtain good effect.
The above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, 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 and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. A machine learning based LC humidity sensor drift compensation method, comprising:
s1, performing a temperature test experiment on an LC humidity sensor to obtain original data, wherein the original data comprises a plurality of groups of data, and each group of data comprises response data of the LC humidity sensor under humidity and temperature conditions;
s2, preprocessing response data of the LC humidity sensor, extracting response bandwidth BW and real part impedance maximum value Re #) Max, and said maximum value Re (>) The method comprises the steps of (1) denoising the resonance frequency Freq corresponding to Max by utilizing wavelet analysis, and carrying out normalization processing on denoised data to establish a data set as a characteristic;
s3, initializing a BP neural network, and optimizing the BP neural network model by adopting a genetic algorithm to obtain a GA-BP model;
s4, the data set comprises a training set and a testing set, the training set is used for training the GA-BP model, and the trained model is used for compensating the testing set of drift data.
2. The method of claim 1, wherein the basis function used for the wavelet analysis in S2 is db10, and the number of decomposition layers of the signal is 3.
3. The machine learning based LC humidity sensor drift compensation method of claim 2 wherein the threshold of the wavelet analysis in S2 is selected as an improved fixed threshold expressed as:
，
the threshold function is selected as an improved threshold function, and the formula of the threshold function is as follows:
，
wherein the method comprises the steps ofAs the standard deviation of the signal,Nfor the length of the data to be the same,jto decompose the layer number->For estimating wavelet coefficients +.>For the decomposed wavelet coefficients sgn is a sign piecewise function.
4. The method of claim 1, wherein the BP neural network in S3 comprises 1 input layer, 2 hidden layers and 1 output layer, the input layer has 3 inputs, and the 2 hidden layers have m and n neurons, respectively.
5. The method for compensating drift of LC humidity sensor based on machine learning as claimed in claim 4, wherein said BP neural network is obtained by linear combination of inputs and then nonlinear transformation by neuron activation function, said neuron activation function is selected from Sigmoid function, mean square error of estimated value and actual measured valueMSEA function as an error function and minimizing the error function using a back propagation algorithm, wherein the mean square errorMSEThe expression of the function is:
wherein,for the actual observations +.>The output value is predicted for the model and,Nis the number of test points.
6. The method of claim 1, wherein the genetic algorithm in S3 optimizes a BP neural network, and wherein the genetic algorithm converges to individuals with high fitness after multiple iterations by continuously evolving individuals in a population, exploring a hyper-parameter space by selecting, crossing and mutating to obtain an optimal hyper-parameter combination, the individuals corresponding to the hyper-parameter combination.
7. The method for LC humidity sensor drift compensation based on machine learning of claim 1 wherein said genetic algorithm optimizes said BP neural network using a decision coefficientAs model evaluation index, coefficient is determinedThe expression of (2) is:
wherein,for the actual observations +.>For the mean value of the actual observations, +.>And predicting an output value for the model, wherein N is the number of test points.
8. The machine learning based LC humidity sensor drift compensation method of claim 1, wherein the BP neural network model optimizes model parameters using Adam optimizer with 10000 iterations.
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