CN111415032A - Method for predicting production performance of polyester fiber precursor based on E L M-AE of transfer learning - Google Patents

Method for predicting production performance of polyester fiber precursor based on E L M-AE of transfer learning Download PDF

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CN111415032A
CN111415032A CN202010141610.9A CN202010141610A CN111415032A CN 111415032 A CN111415032 A CN 111415032A CN 202010141610 A CN202010141610 A CN 202010141610A CN 111415032 A CN111415032 A CN 111415032A
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郝矿荣
张金喜
陈磊
蔡欣
唐雪嵩
王彤
任立红
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Abstract

The invention relates to a polyester fiber precursor production performance prediction method based on E L M-AE of Transfer learning, wherein an E L M network structure only has one hidden layer, the weight of input data and the bias of the hidden layer are randomly generated, and a shallow layer network has a certain deviation on data feature extraction, so that the characteristics of the E L M network structure for extracting data are deepened, and the multiple hidden layers are trained by adopting self-encoding (AE) to improve the training precision of a model.

Description

Method for predicting production performance of polyester fiber precursor based on E L M-AE of transfer learning
Technical Field
The invention belongs to the field of machine learning, and particularly relates to prediction of production performance of polyester fiber strands based on E L M-AE of transfer learning.
Background
The E L M neural network is an approximator of any function, the E L M neural network is a better approximator, and the model has the advantages of easiness in realization, high training speed and strong generalization capability.
An Auto Encoder (AE) is a neural network with only one hidden layer and input and output neurons with the same number of nodes. The self-encoder consists of two parts, wherein one part is an encoding part from an input layer to a hidden layer and is used for extracting the characteristics of input data; the other part is the decoding part, i.e. the hidden layer to the output layer, for reconstructing the data. The self-encoder learning is an unsupervised learning process, an output value is equal to an input value by using a back propagation algorithm, and the self-encoder learning has the functions of reducing noise of data and dimension for visualization and extracting effective data characteristics.
The conventional machine learning problem method assumes that data obeys the same distribution, however, in practical situations, due to the characteristics of difficult data acquisition, high acquisition cost, small number of acquired samples and the like, the migration learning uses data of an unused field with correlation for model learning and improves the generalization performance of the model.
Polyester fibers are synthetic fibers made by direct spinning or remelting spinning of polymers, referred to as PET fibers for short. Polyester fibers have good properties, such as high breaking strength, good elasticity, good heat resistance, wear resistance and light resistance, and stable fabric dimensions, and therefore, polyester fibers are widely used in the fields of industry, agriculture, clothing, home furnishing, and the like. The production yield of the polyester fiber is the second world in China, and is a country with high polyester fiber yield. Therefore, the quality of polyester fiber strand production is receiving attention from researchers. The production process of the polyester fiber protofilament is mainly divided into 3 processes, namely a polymer polymerization process, a melt conveying process and a spinning process. The polymerization process of the polymer is to compound raw materials at high temperature and high pressure to form the polymer in a molten state. The melt conveying part conveys the polymer in a molten state to the spinning process to prevent the polymer from physical change. The spinning process is a process of extruding a polymer in a molten state through a spinning assembly to form filamentous fibers. The melt spinning process of the polyester fiber is the most critical link in the whole production process and determines the quality of the nascent fiber. The spinning process is that the polymer in the molten state is extruded from the capillary holes of a spinneret plate by a metering pump to form liquid trickle, and then the liquid trickle is solidified into filaments by blowing cold air in the air. The polyester fibers with different specifications and varieties have different production lines, the number of spinning positions of each production line, the number of spinnerets in each spinning position and the specification of the spinnerets are different due to different product properties. The spun fiber obtained in the spinning process is fine in quality, the sensor is complex in design and layout and high in cost, and the phenomenon of broken filaments and flying filaments easily occurs in the data acquisition process. At present, because data in the polyester fiber spinning process is difficult to collect, the collection cost is high, the number of collected samples is small, and the like, a better polyester fiber spinning link production process model is difficult to establish. Therefore, the research of establishing a universal model aiming at the polyester fiber spinning process of production lines with different specifications has important significance.
Disclosure of Invention
The invention provides a polyester fiber precursor production performance prediction method based on E L M-AE of transfer learning, which is used for solving the problems that data is difficult to collect, few collected samples are obtained and the like, realizing the performance index prediction of a transfer learning model produced in the polyester fiber spinning process, improving the prediction precision of the model, reducing the production cost of the polyester fiber spinning process and improving the performance and quality of polyester fibers.
In order to achieve the aim, the invention adopts the technical scheme that the method for predicting the production performance of the polyester fiber precursor based on E L M-AE of transfer learning is characterized by comprising the following steps:
(1) establishing a data driving model;
(2) training the weights of the E L M-AE model of the transfer learning;
(3) inputting the production process characteristics of the polyester fiber protofilament to obtain the performance index of the polyester fiber;
the data-driven model is established as described above, and is an E L M data-driven model based on migration self-encoding, the structure of the E L M model is deepened through self-encoding to further extract data features, meanwhile, the source domain input data set and the target domain input data set are subjected to migration learning, and in the encoding process of the self-encoding model, the loss value calculated through the maximum mean difference method in the migration learning is added to the loss function of the self-encoding, the data-driven model is the E L M-AE model of the migration learning, which is referred to as T L-E L M-AE model for short, and the expression of the E L M-AE model target function of the migration learning is as follows:
Figure BDA0002398183720000021
the first two terms on the right side of the equation represent the minimum output weight of the E L M-AE model based on the transfer learning, the third term on the right side of the equation represents the depth extraction data characteristic information, and the fourth term on the right side of the equation represents the process of the transfer learning;
ETL-ELM-AEa loss function of the E L M-AE model representing the transfer learning, X represents input data of the E L M-AE model representing the transfer learning, and X ═ X { [ X ]S,xT}，xSRepresenting a source domain input data set, xTRepresents the target domain input data set, ω represents the input weights of the E L M model, b represents the hidden layer bias of the E L M model,
Figure BDA0002398183720000031
an activation function representing the hidden layer of E L M,
Figure BDA0002398183720000032
representing the output of the E L M hidden layer, β representing the output weight of the E L M model, Y representing the output of the E L M-AE model of the transfer learning, hXInput data representing an AE model, f (-) represents an activation function of a hidden layer of the AE model, f (h)X) Represents the output of the hidden layer of the AE model, g (-) represents the activation function of the output layer of the AE model, g (f (h)X) Represents the output of the AE model output layer; MMD2(-) represents the maximum mean difference, used to calculate the difference between the source domain input data and the target domain input data;
and (3) establishing the E L M-AE model of the transfer learning by solving the loss function minimization of the E L M-AE model of the transfer learning and updating the weight of the E L M-AE model of the transfer learning.
And,
a) the source domain input data set and the target domain input data set are respectively collected from spinning process characteristics of two kinds of polyester fiber protofilament production; the spinning process characteristics of the target domain input data set comprise the spinning process characteristics of the source domain input data set, and the number of samples of each spinning process characteristic of the source domain input data set is far larger than that of each spinning process characteristic of the target domain input data set;
b) the source domain output data set and the target domain output data set are respectively collected from spinning performance indexes produced by two polyester fiber precursors; the spinning performance index of the target domain output data set is contained in the spinning performance index of the source domain output data set, and the number of samples of each spinning performance index of the source domain output data set is far larger than that of each spinning performance index of the target domain output data set;
adding the loss value calculated by the maximum mean difference method in the encoding process of the self-encoding model to the loss function of the self-encoding model, wherein the process is to establish the core content of the E L M-AE model of the transfer learning, and the aim is to calculate the minimum value of the loss function of the transfer self-encoding:
Figure BDA0002398183720000033
wherein E isTL-AELoss function, h, representing migration autocodeXInput data representing an AE model, f (-) represents an activation function of a hidden layer of the AE model, f (h)X) Represents the output of the hidden layer of the AE model, g (-) represents the activation function of the output layer of the AE model, g (f (h)X) Represents the output of the AE model output layer; x is the number ofSInput data, x, representing a source domainTInput data representing a target domain, MMD2(. Maximum Mean variance) is the Maximum Mean difference used to calculate the difference between the source domain input data and the target domain input data, and is calculated as follows:
Figure BDA0002398183720000034
wherein,
Figure BDA0002398183720000041
representing a feature map of source domain input data samples, r representing an r-th sample point of the source domain input data, and p representing a number of samples of the source domain input data;
Figure BDA0002398183720000042
a feature map representing target domain input samples, o represents the o-th sample point of the target domain input data, q represents the target domain inputThe number of samples of incoming data;
the updating formula of the trained weight and bias term is as follows:
Figure BDA0002398183720000043
Figure BDA0002398183720000044
wherein,
Figure BDA0002398183720000045
the representation is a weight value that is migrated from the encoded update,
Figure BDA0002398183720000046
indicating that the migration is from the old weight value of the code,
Figure BDA0002398183720000047
a bias value representing a migrated self-encoded update,
Figure BDA0002398183720000048
offset value representing migration since coding, αTL-AEFor migrating the learning rate of the self-coding, ETL-AEA loss function representing the migrated self-encoding,
Figure BDA0002398183720000049
and
Figure BDA00023981837200000410
the partial derivatives of the loss function of the migrated self-encodings against weight and bias are shown separately.
As a preferred technical scheme:
the method for predicting the production performance of the polyester fiber precursor based on the E L M-AE of the transfer learning is characterized in that the E L M-AE model of the transfer learning is a neural network with a structure larger than 3 layers and comprises an input layer, an H layer hidden layer and an output layer, wherein the value of H is larger than or equal to 2.
The method for predicting the production performance of the polyester fiber strand based on E L M-AE of transfer learning is characterized in that the data set is specifically established as follows:
i, establishing a source domain input data set and a target domain input data set:
the input sample data collected from different production specification batches, namely the process characteristics of the polyester fiber spinning process, form a total input sample data set X ═ XS,xT}，xSSource domain input dataset, x, for represented transfer learningTRepresenting a target domain input data set of transfer learning; a set of source domain input data samples is
Figure BDA00023981837200000411
A set of target domain input data samples is
Figure BDA00023981837200000412
m is the number of process features; thus, the total input sample data set X of N sets of inputs is ═ X1，x2，…，xN]N is p + q, p is the number of source domain samples, and q is the number of target domain samples; the number p of the source domain samples of the transfer learning is far larger than the number q of the target domain samples, and the matrix form of the data set is as follows:
Figure BDA00023981837200000413
each row of the matrix is a process characteristic, and m process characteristics are shared; each column of the matrix is a group of input data, and N groups of input data are provided in total;
Figure BDA0002398183720000051
a value representing the type 1 process characteristic of the type 1 data of the source domain input sample,
Figure BDA0002398183720000052
a value representing the mth process specific of the set 1 of data of the source domain input samples,
Figure BDA0002398183720000053
a value representing a type 1 process characteristic of the p-th set of data of the source domain input samples,
Figure BDA0002398183720000054
a value representing the mth process feature of the pth set of data of the source domain input sample;
Figure BDA0002398183720000055
a value representing the type 1 process characteristic of the type 1 data of the target field input sample,
Figure BDA0002398183720000056
a value representing the mth process feature of the set 1 data of the target field input sample,
Figure BDA0002398183720000057
a value representing the type 1 process characteristic of the qth set of data of the target field input sample,
Figure BDA0002398183720000058
a value representing the mth process feature of the qth set of data of the target field input sample;
standardizing a total input sample data set matrix X, and performing normalization on X in the input data set XSAnd xTRespectively standardizing, wherein a conversion formula of the data standardization treatment is as follows:
Figure BDA0002398183720000059
wherein i is 1, 2, …, m, r is 1, 2, …, p, o is 1, 2, …, q, m is the number of process features, r is the number of input samples of the source domain, and o is the number of input samples of the target domain;
Figure BDA00023981837200000510
inputting the numerical value of the ith process characteristic of the r group of data of the sample data set for the source domain;
Figure BDA00023981837200000511
inputting the mean value of ith process characteristics of a sample data set for a source domain;
Figure BDA00023981837200000512
inputting the standard deviation of the ith process characteristic of the sample data set for a source domain;
Figure BDA00023981837200000513
inputting numerical values of ith process characteristics of the r group of data standardized by the sample data set for a source domain;
Figure BDA00023981837200000514
inputting numerical values of ith process characteristics of the data of the No. data of the sample data set for the target domain;
Figure BDA00023981837200000515
inputting the mean value of ith process characteristics of the sample data set for a target domain;
Figure BDA00023981837200000516
inputting the standard deviation of the ith process characteristic of the sample data set for a target domain;
Figure BDA00023981837200000517
inputting numerical values of ith process characteristics of the data set of the No. o group after the standardization of the sample data set for the target domain;
normalized total input sample dataset matrix, i.e. eigenvector matrix X':
Figure BDA0002398183720000061
each row of the matrix has one process characteristic, and the total number of the process characteristics is m; each column of the matrix is a set of input dataTotal N ═ p + q sets of input data;
Figure BDA0002398183720000062
normalized values representing the set 1 data type 1 process features of the source domain input samples,
Figure BDA0002398183720000063
normalized values representing the mth process feature of the set 1 data of source domain input samples,
Figure BDA0002398183720000064
normalized values representing the p-th set of data of type 1 process features of the source domain input samples,
Figure BDA0002398183720000065
normalized values representing the mth set of data m process features of the source domain input sample;
Figure BDA0002398183720000066
normalized values representing the set 1 data type 1 process features of the target field input sample,
Figure BDA0002398183720000067
normalized values representing the mth process feature of the set 1 data of the target field input sample,
Figure BDA0002398183720000068
normalized values representing the qth set of data of type 1 process features of the target field input sample,
Figure BDA0002398183720000069
normalized values representing the mth set of data m process features of the target domain input sample;
II, establishing a source domain output data set and a target domain output data set:
the performance index of the polyester fiber, which is the output sample data collected in different production specification batches, is used for forming a total output sample data set Y＝{yS,yT}，ySSource domain output dataset, y, representing transfer learningTRepresenting a target domain output data set of the transfer learning; a set of source domain output data samples is
Figure BDA00023981837200000610
A set of target domain output data samples is
Figure BDA00023981837200000611
l is the number of performance indexes; thus, N sets of outputs constitute a total set of output sample data Y ═ Y1，y2，…，yN]N is p + q, p is the number of source domain samples, and q is the number of target domain samples. The number p of the source domain samples of the transfer learning is far larger than the number q of the target domain samples, and the matrix form of the data set is as follows:
Figure BDA00023981837200000612
each row of the matrix has one performance index, the total performance index is l, each column of the matrix is provided with one group of output data, and the total number of the output data is N groups of output data;
Figure BDA00023981837200000613
a value representing the 1 st performance indicator of the 1 st set of data of source domain output samples,
Figure BDA00023981837200000614
a value representing the l performance indicator of the set 1 data of source domain output samples,
Figure BDA00023981837200000615
a value representing the 1 st performance indicator of the p-th set of data of source domain output samples,
Figure BDA00023981837200000616
a value representing a first performance indicator of a p-th set of data of source domain output samples;
Figure BDA00023981837200000617
a value representing the 1 st performance indicator of the 1 st set of data of the target domain output samples,
Figure BDA0002398183720000071
a value representing the l performance indicator of the set 1 data of target domain output samples,
Figure BDA0002398183720000072
a value representing the 1 st performance indicator of the qth set of data of the target domain output samples,
Figure BDA0002398183720000073
a value representing the l performance indicator for the q-th set of data of the target domain output sample;
standardizing the total output sample data set matrix Y, and performing Y in the output data set YSAnd yTThe conversion formula for respectively normalizing the data normalization processes is:
Figure BDA0002398183720000074
wherein i is 1, 2, …, l, r is 1, 2, …, p, o is 1, 2, …, q, l are numbers of performance indexes, r is number of source domain output samples, and o is number of target domain output samples;
Figure BDA0002398183720000075
outputting the numerical value of the ith performance index of the r group of data of the sample data set for the source domain;
Figure BDA0002398183720000076
outputting the mean value of the ith performance index of the sample data set for a source domain;
Figure BDA0002398183720000077
outputting the standard deviation of the ith performance index of the sample data set for a source domain;
Figure BDA0002398183720000078
outputting the numerical value of the ith performance index of the r group of data after the sample data set is standardized for the source domain;
Figure BDA0002398183720000079
outputting the numerical value of the ith data performance index of the sample data set for the target domain;
Figure BDA00023981837200000710
outputting the mean value of the ith performance index of the sample data set for a target domain;
Figure BDA00023981837200000711
outputting the standard deviation of the ith performance index of the sample data set for a target domain;
Figure BDA00023981837200000712
outputting the numerical value of the ith performance index of the group o data after the sample data set is standardized for the target domain;
the normalized total output sample data set matrix, i.e., the performance index matrix Y':
Figure BDA00023981837200000713
one performance index is used for each behavior of the matrix, and the total performance index is l; each column of the matrix is a group of input data, and N is p + q groups of output data;
Figure BDA00023981837200000714
normalized values for the 1 st data performance index of the 1 st data set representing source domain output samples,
Figure BDA00023981837200000715
representing source domain output samplesNormalized values for the first performance index for group 1 data,
Figure BDA00023981837200000716
normalized values for the 1 st performance index of the p-th set of data representing source domain output samples,
Figure BDA0002398183720000081
a normalized value representing a set p of data l performance indicators of the source domain output sample;
Figure BDA0002398183720000082
normalized values for the 1 st data performance index of the 1 st data set representing the target domain output sample,
Figure BDA0002398183720000083
normalized values for the l performance indicators of the set 1 data representing target domain output samples,
Figure BDA0002398183720000084
normalized values for the 1 st performance index of the qth set of data representing the target domain output samples,
Figure BDA0002398183720000085
normalized values representing the data l performance index of the q-th set of data of the target domain output samples.
The method for predicting the production performance of the polyester fiber precursors based on the E L M-AE based on the transfer learning is characterized in that the weights of the E L M-AE model based on the transfer learning are specifically that the process feature matrix X 'is used as an input sample matrix of the E L M-AE model based on the transfer learning, the output sample data set matrix Y' is used as an output sample matrix of the E L M-AE model based on the transfer learning, the process feature matrix is applied to the E L M-AE model based on the transfer learning, a training sample input data set is input to a first layer of the E L M-AE model based on the transfer learning, the link weights of each layer of the E L M-AE model based on the transfer learning are in a full-connection mode, the E L M-AE model based on the transfer learning has H hidden layers, the weights of a front H-1 layer of the model are obtained by transfer self-coding training and are used for deeply extracting the features of the training data and reducing the differences among different domains, and the weights of the H layer and the weights of the original E L M-AE model based on the transfer learning guarantee method.
The method for predicting the production performance of the polyester fiber precursor based on E L M-AE of transfer learning is characterized in that the number N of input sample groups is 200-1000, wherein N is p + q, p represents the number of samples of source domain data, q represents the number of target domain samples, the number p of the source domain data samples is far larger than the number of the target domain samples q, the number M of process characteristics in the spinning process is 1-8, and the number l of different performance indexes affecting the quality of the polyester fiber is 1-6.
The method for predicting the production performance of the polyester fiber precursor based on E L M-AE of transfer learning is characterized in that the number M of process characteristics of the spinning process is 4, namely the spinning speed, the spinning temperature, the blowing speed and the blowing temperature, and the number l of different performance indexes influencing the quality of the polyester fiber is 4, namely the half-time elongation (EYS 1.5.5), the elongation unevenness (EYSCV), the breaking strength (DT) and the elongation capability (DE).
The method for predicting the production performance of the polyester fiber precursor based on the E L M-AE obtained by the transfer learning is characterized in that a multilayer AE model is adopted for extracting the data characteristics of the E L M-AE model obtained by the transfer learning, the AE model calculates the connection weight for adjusting each layer of the network through an inverse error, and the aim is to make the output data of the AE model closer to the input data, namely minimize the loss function error of the AE model:
Figure BDA0002398183720000086
Figure BDA0002398183720000087
wherein E isAELoss function, X, representing input data and output data of AE modelAEInput data representing an AE model, f (-) is a hidden layer activation function of the AE model, g (-) is an output layer activation function of the AE model, WinAnd WoutConnecting weights for input and output layers of the AE model, binAnd boutInput and output layer bias terms for the AE model, HAEThe output representing the hidden layer of the AE model, i.e. the result of the encoding process of the AE model,
Figure BDA0002398183720000091
represents the output of the AE model output layer, i.e., the result of the decoding process of the AE model;
by solving the loss function of the AE model, the updating formula of the weight and the bias term of the AE model is as follows:
Figure BDA0002398183720000092
Figure BDA0002398183720000093
wherein,
Figure BDA0002398183720000094
the representation is a weight of the AE model update,
Figure BDA0002398183720000095
the old weight of the AE model is represented,
Figure BDA0002398183720000096
the offset representing the update of the AE model,
Figure BDA0002398183720000097
representing the old offset of the AE model, αAEAs the learning rate of the AE model,
Figure BDA0002398183720000098
and
Figure BDA0002398183720000099
the partial derivatives of the loss function of the AE model with respect to weight and bias are shown, respectively.
Drawings
FIG. 1 is a structural view of a polyester fiber strand productivity prediction method based on E L M-AE of migration learning;
FIG. 2 is a diagram of the AE neural network architecture;
FIG. 3 is a diagram of a neural network architecture for transfer learning E L M-AE;
FIG. 4 is a comparison of the actual value of the elongation at half-maximum (EYS 1.5.5) to the predicted result;
FIG. 5 is a comparison of the real values of the elongation unevenness (EYSCV) with the predicted results;
FIG. 6 is a comparison of the actual value of the breaking strength (DT) with the predicted result;
FIG. 7 is a comparison of the real value of the elongation ability (DE) with the predicted result.
Detailed Description
The invention will be further illustrated with reference to specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
Example 1
The method comprises the steps of collecting process characteristic data of four spinning processes including spinning temperature, spinning speed, blowing temperature and blowing speed of polyester fiber spinning as input of an E L M-AE model of migration learning, collecting performance indexes such as half-elongation (EYS.5), elongation-unevenness (EYSCV), breaking strength (DT) and elongation ability (DE) of the polyester fiber as output of an E L M-AE model of migration learning, collecting process characteristic data of two production lines A and B of different specifications of the polyester fiber in the production process of the polyester fiber, wherein the quantity of each input data and each output data collected by the production line A is 216, wherein the quantity of the data is shown as a source domain input data for establishing an E L M-AE model of migration learning, the data is shown as a source domain input data of the production line A632 and a source domain output data of the production line B is shown as an E L M-AE model of migration learning, the data is shown as a source domain input data of the production line A632 and the source domain output data of the E L M630M 6313M 6312, the source domain input data and the E632 as an E domain input data and the E3 as an E equivalent to the migration learning model, the migration learning result of migration learning model is shown as an E equivalent to a migration learning model, the migration learning model is shown as an E equivalent to a migration learning model of migration learning model, the migration learning model of migration learning model is shown as an E equivalent to the migration learning model, the migration learning model of migration learning model, the migration learning model is shown as an E equivalent to the migration learning model, the migration learning model of migration learning model, the migration learning model is shown as an E equivalent to the migration learning model, the migration learning model is shown as an E equivalent to the migration learning model, the migration learning model is shown as an equivalent to the migration learning model.
TABLE 1 number of samples collected for each process feature sample point of the input data set
Figure BDA0002398183720000101
TABLE 2 number of samples collected per performance index of the output data set
Figure BDA0002398183720000102
Table 3 number of acquisitions per sample point of the validation data set
Figure BDA0002398183720000111

Claims (7)

1. The polyester fiber protofilament production performance prediction method based on E L M-AE of transfer learning is characterized by comprising the steps of (1) establishing a data-driven model, (2) training the weight of the E L M-AE model of transfer learning, (3) inputting polyester fiber protofilament production process characteristics to obtain polyester fiber performance indexes;
the data driving model is established by establishing an E L M-AE data driving model based on transfer learning, the structure of the E L M model is deepened through self-coding to further extract data characteristics, meanwhile, transfer learning is carried out on a source domain input data set and a target domain input data set, in the coding process of the self-coding model, a loss value calculated through a maximum mean difference method in the transfer learning is added to a self-coding loss function, the data driving model is the E L M-AE model of the transfer learning, which is called T L-E L M-AE model for short, and the expression of the E L M-AE model target function of the transfer learning is as follows:
Figure FDA0002398183710000011
the first two terms on the right side of the equation represent the minimum output weight of the E L M-AE model based on the transfer learning, the third term on the right side of the equation represents the depth extraction data characteristic information, and the fourth term on the right side of the equation represents the process of the transfer learning;
ETL-ELM-AEa loss function of the E L M-AE model representing the transfer learning, X represents input data of the E L M-AE model representing the transfer learning, and X ═ X { [ X ]S,xT}，xSRepresenting a source domain input data set, xTRepresents the target domain input data set, ω represents the input weights of the E L M model, b represents the hidden layer bias of the E L M model,
Figure FDA0002398183710000012
an activation function representing the hidden layer of E L M,
Figure FDA0002398183710000013
representing the output of the E L M hidden layer, β representing the output weight of the E L M model, Y representing the output of the E L M-AE model of the transfer learning, hXInput data representing an AE model, f (-) represents an activation function of a hidden layer of the AE model, f (h)X) Watch (A)Showing the output of the hidden layer of the AE model, g (-) showing the activation function of the output layer of the AE model, g (f (h)X) Represents the output of the AE model output layer; MMD2(-) represents the maximum mean difference, used to calculate the difference between the source domain input data and the target domain input data;
the method comprises the steps of establishing a transfer learning E L M-AE model by solving the loss function minimization of the transfer learning E L M-AE model and updating the weight of the transfer learning E L M-AE model;
and,
a) the source domain input data set and the target domain input data set are respectively collected from spinning process characteristics of two kinds of polyester fiber protofilament production; the spinning process characteristics of the target domain input data set comprise the spinning process characteristics of the source domain input data set, and the number of samples of each spinning process characteristic of the source domain input data set is far larger than that of each spinning process characteristic of the target domain input data set;
b) the source domain output data set and the target domain output data set are respectively collected from spinning performance indexes produced by two polyester fiber precursors; the spinning performance index of the target domain output data set is contained in the spinning performance index of the source domain output data set, and the number of samples of each spinning performance index of the source domain output data set is far larger than that of each spinning performance index of the target domain output data set;
adding the loss value calculated by the maximum mean difference method in the encoding process of the self-encoding model to the loss function of the self-encoding model, wherein the process is to establish the core content of the E L M-AE model of the transfer learning, and the aim is to calculate the minimum value of the loss function of the transfer self-encoding:
Figure FDA0002398183710000021
wherein E isTL-AELoss function, h, representing migration autocodeXInput data representing an AE model, f (-) represents an activation function of a hidden layer of the AE model, f (h)X) Represents the output of the hidden layer of the AE model, and g (-) represents the AE modelActivation function of the output layer, g (h)X) Represents the output of the AE model output layer; x is the number ofSInput data, x, representing a source domainTInput data representing a target domain, MMD2(. h) is the maximum mean difference, which is used to calculate the difference between the source domain input data and the target domain input data, and is calculated as follows:
Figure FDA0002398183710000022
wherein,
Figure FDA0002398183710000023
representing a feature map of source domain input data samples, r representing an r-th sample point of the source domain input data, and p representing a number of samples of the source domain input data;
Figure FDA00023981837100000212
representing a feature map of target domain input samples, o representing an o-th sample point of the target domain input data, q representing a number of samples of the target domain input data;
the updating formula of the trained weight and bias term is as follows:
Figure FDA0002398183710000024
Figure FDA0002398183710000025
wherein,
Figure FDA0002398183710000026
the representation is a weight value that is migrated from the encoded update,
Figure FDA0002398183710000027
indicating that the migration is from the old weight value of the code,
Figure FDA0002398183710000028
a bias value representing a migrated self-encoded update,
Figure FDA0002398183710000029
offset value representing migration since coding, αTL-AEFor migrating the learning rate of the self-coding, ETL-AEA loss function representing the migrated self-encoding,
Figure FDA00023981837100000210
and
Figure FDA00023981837100000211
the partial derivatives of the loss function of the migrated self-encodings against weight and bias are shown separately.
2. The method for predicting the production performance of the polyester fiber precursors based on the transfer-learning E L M-AE of claim 1, wherein the transfer-learning E L M-AE model is a neural network with a structure larger than 3 layers and comprises an input layer, an H hidden layer and an output layer, and the value of H is larger than or equal to 2.
3. The method for predicting the production performance of polyester fiber strands based on E L M-AE obtained by transfer learning according to claim 1, wherein the data set is created by:
i, establishing a source domain input data set and a target domain input data set:
the input sample data collected from different production specification batches, namely the process characteristics of the polyester fiber spinning process, form a total input sample data set X ═ XS,xT}，xSSource domain input dataset, x, for represented transfer learningTRepresenting a target domain input data set of transfer learning; a set of source domain input data samples is
Figure FDA0002398183710000031
A set of target domain input data samples is
Figure FDA0002398183710000032
m is the number of process features; thus, the total input sample data set X of N sets of inputs is ═ X1，x2，…，xN]N is p + q, p is the number of source domain samples, and q is the number of target domain samples; the number p of the source domain samples of the transfer learning is far larger than the number q of the target domain samples, and the matrix form of the data set is as follows:
Figure FDA0002398183710000033
each row of the matrix is a process characteristic, and m process characteristics are shared; each column of the matrix is a group of input data, and N groups of input data are provided in total;
Figure FDA0002398183710000034
a value representing the type 1 process characteristic of the type 1 data of the source domain input sample,
Figure FDA0002398183710000035
a value representing the mth process specific of the set 1 of data of the source domain input samples,
Figure FDA0002398183710000036
a value representing a type 1 process characteristic of the p-th set of data of the source domain input samples,
Figure FDA0002398183710000037
a value representing the mth process feature of the pth set of data of the source domain input sample;
Figure FDA0002398183710000038
a value representing the type 1 process characteristic of the type 1 data of the target field input sample,
Figure FDA0002398183710000039
data 1 of the group representing target field input samplesThe values of the m process characteristics are,
Figure FDA00023981837100000310
a value representing the type 1 process characteristic of the qth set of data of the target field input sample,
Figure FDA00023981837100000311
a value representing the mth process feature of the qth set of data of the target field input sample;
standardizing a total input sample data set matrix X, and performing normalization on X in the input data set XSAnd xTRespectively standardizing, wherein a conversion formula of the data standardization treatment is as follows:
Figure FDA00023981837100000312
wherein i is 1, 2, …, m, r is 1, 2, …, p, o is 1, 2, …, q, m is the number of process features, r is the number of input samples of the source domain, and o is the number of input samples of the target domain;
Figure FDA0002398183710000041
inputting the numerical value of the ith process characteristic of the r group of data of the sample data set for the source domain;
Figure FDA0002398183710000042
inputting the mean value of ith process characteristics of a sample data set for a source domain;
Figure FDA0002398183710000043
inputting the standard deviation of the ith process characteristic of the sample data set for a source domain;
Figure FDA0002398183710000044
input of ith process characteristics of data set of r group normalized by sample data set for source domainA numerical value;
Figure FDA0002398183710000045
inputting numerical values of ith process characteristics of the data of the No. data of the sample data set for the target domain;
Figure FDA0002398183710000046
inputting the mean value of ith process characteristics of the sample data set for a target domain;
Figure FDA0002398183710000047
inputting the standard deviation of the ith process characteristic of the sample data set for a target domain;
Figure FDA0002398183710000048
inputting numerical values of ith process characteristics of the data set of the No. o group after the standardization of the sample data set for the target domain;
normalized total input sample dataset matrix, i.e. eigenvector matrix X':
Figure FDA0002398183710000049
each row of the matrix has one process characteristic, and the total number of the process characteristics is m; each column of the matrix is a group of input data, and N is p + q groups of input data;
Figure FDA00023981837100000410
normalized values representing the set 1 data type 1 process features of the source domain input samples,
Figure FDA00023981837100000411
normalized values representing the mth process feature of the set 1 data of source domain input samples,
Figure FDA00023981837100000412
normalized values representing the p-th set of data of type 1 process features of the source domain input samples,
Figure FDA00023981837100000413
normalized values representing the mth set of data m process features of the source domain input sample;
Figure FDA00023981837100000414
normalized values representing the set 1 data type 1 process features of the target field input sample,
Figure FDA00023981837100000415
normalized values representing the mth process feature of the set 1 data of the target field input sample,
Figure FDA00023981837100000416
normalized values representing the qth set of data of type 1 process features of the target field input sample,
Figure FDA00023981837100000417
normalized values representing the mth set of data m process features of the target domain input sample;
II, establishing a source domain output data set and a target domain output data set:
the method comprises the steps of collecting output sample data of different production specification batches, namely the performance index of the polyester fiber, and forming a total output sample data set of Y ═ YS,yT}，ySSource domain output dataset, y, representing transfer learningTRepresenting a target domain output data set of the transfer learning; a set of source domain output data samples is
Figure FDA0002398183710000051
A set of target domain output data samples is
Figure FDA0002398183710000052
l is the number of performance indexes; thus, N sets of outputs constitute a total set of output sample data Y ═ Y1，y2，…，yN]N is p + q, p is the number of source domain samples, and q is the number of target domain samples; the number p of the source domain samples of the transfer learning is far larger than the number q of the target domain samples, and the matrix form of the data set is as follows:
Figure FDA0002398183710000053
each row of the matrix has one performance index, the total performance index is l, each column of the matrix is provided with one group of output data, and the total number of the output data is N groups of output data;
Figure FDA0002398183710000054
a value representing the 1 st performance indicator of the 1 st set of data of source domain output samples,
Figure FDA0002398183710000055
a value representing the l performance indicator of the set 1 data of source domain output samples,
Figure FDA0002398183710000056
a value representing the 1 st performance indicator of the p-th set of data of source domain output samples,
Figure FDA0002398183710000057
a value representing a first performance indicator of a p-th set of data of source domain output samples;
Figure FDA0002398183710000058
a value representing the 1 st performance indicator of the 1 st set of data of the target domain output samples,
Figure FDA0002398183710000059
a value representing the l performance indicator of the set 1 data of target domain output samples,
Figure FDA00023981837100000510
a value representing the 1 st performance indicator of the qth set of data of the target domain output samples,
Figure FDA00023981837100000511
a value representing the l performance indicator for the q-th set of data of the target domain output sample;
standardizing the total output sample data set matrix Y, and performing Y in the output data set YSAnd yTThe conversion formula for respectively normalizing the data normalization processes is:
Figure FDA00023981837100000512
wherein i is 1, 2, …, l, r is 1, 2, …, p, o is 1, 2, …, q, l are numbers of performance indexes, r is number of source domain output samples, and o is number of target domain output samples;
Figure FDA00023981837100000513
outputting the numerical value of the ith performance index of the r group of data of the sample data set for the source domain;
Figure FDA00023981837100000514
outputting the mean value of the ith performance index of the sample data set for a source domain;
Figure FDA0002398183710000061
outputting the standard deviation of the ith performance index of the sample data set for a source domain;
Figure FDA0002398183710000062
outputting the numerical value of the ith performance index of the r group of data after the sample data set is standardized for the source domain;
Figure FDA0002398183710000063
outputting the numerical value of the ith data performance index of the sample data set for the target domain;
Figure FDA0002398183710000064
outputting the mean value of the ith performance index of the sample data set for a target domain;
Figure FDA0002398183710000065
outputting the standard deviation of the ith performance index of the sample data set for a target domain;
Figure FDA0002398183710000066
outputting the numerical value of the ith performance index of the group o data after the sample data set is standardized for the target domain;
the normalized total output sample data set matrix, i.e., the performance index matrix Y':
Figure FDA0002398183710000067
one performance index is used for each behavior of the matrix, and the total performance index is l; each column of the matrix is a group of input data, and N is p + q groups of output data;
Figure FDA0002398183710000068
normalized values for the 1 st data performance index of the 1 st data set representing source domain output samples,
Figure FDA0002398183710000069
a normalized value of the l performance indicator for the set 1 data representing source domain output samples,
Figure FDA00023981837100000610
representing source domain output samplesThe normalized values of the 1 st performance index of the p-th group of data,
Figure FDA00023981837100000611
a normalized value representing a set p of data l performance indicators of the source domain output sample;
Figure FDA00023981837100000612
normalized values for the 1 st data performance index of the 1 st data set representing the target domain output sample,
Figure FDA00023981837100000613
normalized values for the l performance indicators of the set 1 data representing target domain output samples,
Figure FDA00023981837100000614
normalized values for the 1 st performance index of the qth set of data representing the target domain output samples,
Figure FDA00023981837100000615
normalized values representing the data l performance index of the q-th set of data of the target domain output samples.
4. The method for predicting the production performance of polyester fiber strands based on E L M-AE in transfer learning of claim 3, wherein the weights for training the E L M-AE model in transfer learning are specifically that the process feature matrix X 'is used as an input sample matrix of the E L M-AE model in transfer learning, the output sample data set matrix Y' is used as an output sample matrix of the E L M-AE model in transfer learning, the E L M-AE model in transfer learning is applied, a training sample input data set is input to a first layer of the E L M-AE model in transfer learning, the link weights of each layer of the E L M-AE model in transfer learning are in a fully connected mode, the E L M-AE model in transfer learning shares H hidden layers, the weights of the front H-1 layer of the model are obtained by transfer self-coding training and are used for deeply extracting the features of the training data and reducing the performance difference between different domains, and the weight difference of the H layer and the output weight layer are calculated by the original E L M-AE model.
5. The method for predicting the production performance of polyester fiber precursors based on E L M-AE of transfer learning of claim 3, wherein the number N of input sample groups is 200-1000, wherein N is p + q, p represents the number of samples of source domain data, q represents the number of target domain samples, the number p of source domain data samples is far greater than the number of target domain samples q, the number M of process characteristics in the spinning process is 1-8, and the number l of different performance indexes affecting the quality of polyester fibers is 1-6.
6. The method for predicting the production performance of polyester fiber precursors based on E L M-AE in claim 5, wherein the number of process characteristics M of the spinning process is 4, which are the spinning speed, the spinning temperature, the blowing speed and the blowing temperature, respectively, and the number of different performance indexes l affecting the quality of polyester fibers is 4, which are the elongation at half maximum, the elongation at break, the breaking strength and the elongation ability, respectively.
7. The method for predicting production performance of polyester fiber precursors based on E L M-AE in claim 1, wherein the data characteristics of E L M-AE model are extracted by using a multi-layer AE model, the AE model calculates the connection weight for adjusting each layer by inverse error, and the aim is to make the output data of the AE model closer to the input data, namely the AE model loss function error is minimized:
Figure FDA0002398183710000071
Figure FDA0002398183710000072
wherein E isAELoss function, X, representing input data and output data of AE modelAEDenotes the AE modelType I data, f (-) is the hidden layer activation function of the AE model, g (-) is the output layer activation function of the AE model, WinAnd WoutConnecting weights for input and output layers of the AE model, binAnd boutInput and output layer bias terms for the AE model, HAEThe output representing the hidden layer of the AE model, i.e. the result of the encoding process of the AE model,
Figure FDA0002398183710000073
represents the output of the AE model output layer, i.e., the result of the decoding process of the AE model;
by solving the loss function of the AE model, the updating formula of the weight and the bias term of the AE model is as follows:
Figure FDA0002398183710000074
Figure FDA0002398183710000075
wherein,
Figure FDA0002398183710000076
the representation is a weight of the AE model update,
Figure FDA0002398183710000077
the old weight of the AE model is represented,
Figure FDA0002398183710000078
the offset representing the update of the AE model,
Figure FDA0002398183710000079
representing the old offset of the AE model, αAEAs the learning rate of the AE model,
Figure FDA00023981837100000710
and
Figure FDA00023981837100000711
the partial derivatives of the loss function of the AE model with respect to weight and bias are shown, respectively.
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