CN114722723B - Emotion tendency prediction method and equipment based on kernel extreme learning machine optimization - Google Patents
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Abstract
The invention provides an emotion tendency prediction method and equipment based on kernel extreme learning machine optimization. The method comprises the following steps: step 1 to step 8. According to the invention, multi-objective optimization can be performed by integrating multi-objective scenes, the multi-objective optimization problem is reduced to form a double-objective optimization problem through objectives, the optimal kernel extreme learning machine parameter combination can be obtained by utilizing the optimizing capability and the faster convergence speed of a seagull optimization algorithm and combining with a multi-objective optimization strategy, and the prediction model can be established to improve the emotion tendency prediction accuracy.
Description
Technical Field
The embodiment of the invention relates to the technical field of computer networks, in particular to an emotion tendency prediction method and equipment based on kernel extreme learning machine optimization.
Background
With the continuous development of the Internet, mobile phones and computers are increasingly popularized to provide an omnibearing sounding channel for wide netizens. Different netizens comment with different insights, and their emotional tendency can be about the development progress of many events. The biggest characteristic of the network is anonymity and popularity, which provides opportunities for a plurality of unknown websites, and in order to increase the awareness and the attention, or merchants hiring to pursue benefits often conduct false propaganda, thereby affecting the correct trend of public opinion and deviating the original correct public opinion from the positive rail. For this purpose, monitoring and guidance of network public opinion is an important task for the relevant departments. Compared with other traditional public opinion, the network public opinion has a larger propagation range and a faster propagation speed. Therefore, the network public opinion can express the heart sounds of the masses of netizens most directly and rapidly, so that the society can more intuitively and timely know the heart sounds of the masses. The data generated by the current network public opinion is more scattered, how to perform centralized processing on big data and accurately extract useful information from the big data is a great challenge for the current-stage social public opinion management and control. Therefore, developing a method and a device for predicting emotion tendencies based on kernel extreme learning machine optimization can effectively overcome the defects in the related technology, and becomes a technical problem to be solved in the industry.
Disclosure of Invention
Aiming at the problems existing in the prior art, the embodiment of the invention provides a method and equipment for predicting emotion tendencies based on kernel extreme learning machine optimization.
In a first aspect, an embodiment of the present invention provides an emotion tendency prediction method based on kernel extreme learning machine optimization, including: step 1: processing an emotion tendency prediction data set, mainly comprising data cleaning, data integration, data conversion and part of speech tagging, constructing a word cloud picture, knowing word meaning and part of speech distribution, and segmenting the data set; step 2: the method comprises the steps that a core extreme learning machine combines a core learning theory with an optimization method of the extreme learning machine to construct a core extreme learning machine model; step 3: determining multi-objective function and kernel extreme learning machine optimization parameters, including initial weights and thresholds; step 4: defining a multi-target optimization strategy, and searching target subsets, wherein the target subsets keep or change the pareto set advantage structure of the original problem as little as possible; step 5: searching for a pareto solution by using a seagull optimization algorithm; step 6: judging whether the current iteration times reach the maximum iteration times Max_iter, and if so, outputting the optimal parameter combination of the multi-objective optimization kernel extreme learning machine; if not, returning to the step 5 to continue iteration; step 7: establishing an emotion tendency prediction model according to the obtained optimal parameter set of the kernel extreme learning machine, and training and modeling the emotion tendency prediction model by adopting a training data set; step 8: and testing the trained emotion tendency prediction model by adopting a test set, and verifying the validity of the emotion tendency prediction model.
Based on the content of the method embodiment, the emotion tendency prediction method based on the optimization of the kernel extreme learning machine provided by the embodiment of the invention combines the kernel learning theory and the optimization method of the extreme learning machine to construct a kernel extreme learning machine model, wherein the kernel extreme learning machine model comprises an activation function of the kernel extreme learning machine:
h(x)＝k(xi,xj)
Where k (x i,xj) is a line_kernel, rbf_kernel, poly_kernel, or sigmoid_kernel type, and h (x) is an activation function of the kernel extreme learning machine model.
Based on the content of the method embodiment, the emotion tendency prediction method based on the optimization of the kernel extreme learning machine provided by the embodiment of the invention combines the kernel learning theory and the optimization method of the extreme learning machine to construct a kernel extreme learning machine model, wherein the kernel extreme learning machine model comprises the kernel extreme learning machine model:
wherein H is an hidden layer matrix, H T is the generalized inverse of the hidden layer matrix, T is a predicted target vector, And C is a random coefficient, wherein the sum of elements and offset amounts on opposite angles in the symmetric matrix.
Based on the content of the embodiment of the method, the emotion tendency prediction method based on kernel extreme learning machine optimization provided by the embodiment of the invention comprises the following steps:
Wherein obf 1 is a mean square error, obf 2 is a standard deviation, N represents the number of samples, y i represents the ith predicted value, y i represents the ith predicted value, min is the minimum value, and N is the number of predicted values.
Based on the content of the method embodiment, the emotion tendency prediction method based on kernel extreme learning machine optimization provided in the embodiment of the present invention, wherein the target subset in step 4 retains or changes the pareto set advantage structure of the original problem as little as possible, and the method comprises the following steps: defining an external archive for storing the optimal non-dominant pareto solution, determining whether to classify a solution as an optimal non-dominant pareto solution; the nearest neighbor heuristic takes the individual closest to the current individual as the next individual.
Based on the content of the embodiment of the method, the emotion tendency prediction method based on kernel extreme learning machine optimization provided by the embodiment of the invention comprises the following index functions:
Where F ij (μ) represents the distance between individuals i and j, μ represents the weight coefficient, the search distance and time are balanced, F represents the transposed matrix, d ij represents the distance between two points i and j, and V represents the target space.
Based on the content of the method embodiment, the emotion tendency prediction method based on kernel extreme learning machine optimization provided by the embodiment of the invention utilizes a seagull optimization algorithm to search for a pareto solution, and comprises the following steps: the attack and migration of seagulls are utilized to find out the optimal population individuals and the positions; substituting the optimal target optimization function to find the optimal non-dominant pareto solution.
In a second aspect, an embodiment of the present invention provides an emotion tendency prediction apparatus based on kernel extreme learning machine optimization, including: the first main module is configured to implement step 1: processing an emotion tendency prediction data set, mainly comprising data cleaning, data integration, data conversion and part of speech tagging, constructing a word cloud picture, knowing word meaning and part of speech distribution, and segmenting the data set; the second main module is configured to implement step 2: the method comprises the steps that a core extreme learning machine combines a core learning theory with an optimization method of the extreme learning machine to construct a core extreme learning machine model; a third main module, configured to implement step 3: determining multi-objective function and kernel extreme learning machine optimization parameters, including initial weights and thresholds; a fourth main module, configured to implement step 4: defining a multi-target optimization strategy, and searching target subsets, wherein the target subsets keep or change the pareto set advantage structure of the original problem as little as possible; a fifth main module, configured to implement step 5: searching for a pareto solution by using a seagull optimization algorithm; a sixth main module, configured to implement step 6: judging whether the current iteration times reach the maximum iteration times Max_iter, and if so, outputting the optimal parameter combination of the multi-objective optimization kernel extreme learning machine; if not, returning to the step 5 to continue iteration; seventh main module, configured to implement step 7: establishing an emotion tendency prediction model according to the obtained optimal parameter set of the kernel extreme learning machine, and training and modeling the emotion tendency prediction model by adopting a training data set; eighth main module, configured to implement step 8: and testing the trained emotion tendency prediction model by adopting a test set, and verifying the validity of the emotion tendency prediction model.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
At least one processor; and
At least one memory communicatively coupled to the processor, wherein:
The memory stores program instructions executable by the processor, the processor invoking the program instructions capable of executing the emotion tendency prediction method based on kernel extreme learning machine optimization provided by any of the various implementations of the first aspect.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the emotion tendency prediction method based on kernel extreme learning machine optimization provided by any of the various implementations of the first aspect.
According to the emotion tendency prediction method and equipment based on kernel extreme learning machine optimization, multi-objective optimization can be conducted through multi-objective scenes, the multi-objective optimization problem is reduced to form a double-objective optimization problem through objectives, the optimal kernel extreme learning machine parameter combination can be obtained by utilizing the optimizing capability and the faster convergence rate of a seagull optimization algorithm and combining with the multi-objective optimization strategy, and the prediction model can be established to improve the emotion tendency prediction accuracy.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description will be given below of the drawings required for the embodiments or the prior art descriptions, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without any inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an emotion tendency prediction method based on kernel extreme learning machine optimization provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of an emotion tendency prediction device based on kernel extreme learning machine optimization according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention;
fig. 4 is a model diagram of an emotion tendency prediction method based on kernel extreme learning machine optimization according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. In addition, the technical features of each embodiment or the single embodiment provided by the invention can be combined with each other at will to form a feasible technical scheme, and the combination is not limited by the sequence of steps and/or the structural composition mode, but is necessarily based on the fact that a person of ordinary skill in the art can realize the combination, and when the technical scheme is contradictory or can not realize, the combination of the technical scheme is not considered to exist and is not within the protection scope of the invention claimed.
The embodiment of the invention provides an emotion tendency prediction method based on kernel extreme learning machine optimization, which is shown in fig. 1, and comprises the following steps: step 1: processing an emotion tendency prediction data set, mainly comprising data cleaning, data integration, data conversion and part of speech tagging, constructing a word cloud picture, knowing word meaning and part of speech distribution, and segmenting the data set; step 2: the method comprises the steps that a core extreme learning machine combines a core learning theory with an optimization method of the extreme learning machine to construct a core extreme learning machine model; step 3: determining multi-objective function and kernel extreme learning machine optimization parameters, including initial weights and thresholds; step 4: defining a multi-target optimization strategy, and searching target subsets, wherein the target subsets keep or change the pareto set advantage structure of the original problem as little as possible; step 5: searching for a pareto solution by using a seagull optimization algorithm; step 6: judging whether the current iteration times reach the maximum iteration times Max_iter, and if so, outputting the optimal parameter combination of the multi-objective optimization kernel extreme learning machine; if not, returning to the step 5 to continue iteration; step 7: establishing an emotion tendency prediction model according to the obtained optimal parameter set of the kernel extreme learning machine, and training and modeling the emotion tendency prediction model by adopting a training data set; step 8: and testing the trained emotion tendency prediction model by adopting a test set, and verifying the validity of the emotion tendency prediction model.
Based on the content of the method embodiment, as an optional embodiment, the emotion tendency prediction method based on the optimization of the kernel extreme learning machine provided by the embodiment of the invention, wherein the kernel extreme learning machine combines the kernel learning theory and the optimization method of the extreme learning machine to construct a kernel extreme learning machine model, and the kernel extreme learning machine model comprises an activation function of the kernel extreme learning machine:
h(x)＝k(xi,xj)
Where k (x i,xj) is a line_kernel, rbf_kernel, poly_kernel, or sigmoid_kernel type, and h (x) is an activation function of the kernel extreme learning machine model.
Based on the content of the method embodiment, as an optional embodiment, the emotion tendency prediction method based on the optimization of the kernel extreme learning machine provided by the embodiment of the invention, wherein the kernel extreme learning machine combines the kernel learning theory and the optimization method of the extreme learning machine to construct a kernel extreme learning machine model, and the kernel extreme learning machine model comprises the kernel extreme learning machine model:
wherein H is an hidden layer matrix, H T is the generalized inverse of the hidden layer matrix, T is a predicted target vector, And C is a random coefficient, wherein the sum of elements and offset amounts on opposite angles in the symmetric matrix.
Based on the content of the above method embodiment, as an optional embodiment, the emotion tendency prediction method based on kernel extreme learning machine optimization provided in the embodiment of the present invention, the multiple objective functions in step 3 include:
Wherein obf 1 is a mean square error, obf 2 is a standard deviation, N represents the number of samples, y i represents the ith predicted value, y i represents the ith predicted value, min is the minimum value, and N is the number of predicted values.
Based on the content of the above method embodiment, as an optional embodiment, the emotion tendency prediction method based on kernel extreme learning machine optimization provided in the embodiment of the present invention, the target subset in step 4 retains or changes the pareto set advantage structure of the original problem as little as possible, and includes: defining an external archive for storing the optimal non-dominant pareto solution, determining whether to classify a solution as an optimal non-dominant pareto solution; the nearest neighbor heuristic takes the individual closest to the current individual as the next individual.
Based on the content of the above method embodiment, as an optional embodiment, the emotion tendency prediction method based on kernel extreme learning machine optimization provided in the embodiment of the present invention includes:
where F ij (μ) represents the distance between individuals i and j, μ represents the weight coefficient, the search distance and time are balanced, F represents the transposed matrix, d ij represents the distance between two points i and j, and V represents the target space.
Based on the content of the above method embodiment, as an optional embodiment, the emotion tendency prediction method based on kernel extreme learning machine optimization provided in the embodiment of the present invention, the searching for pareto solutions by using a seagull optimization algorithm includes: the attack and migration of seagulls are utilized to find out the optimal population individuals and the positions; substituting the optimal target optimization function to find the optimal non-dominant pareto solution.
According to the emotion tendency prediction method based on kernel extreme learning machine optimization, multi-objective optimization can be performed by integrating multi-objective scenes, the multi-objective optimization problem is reduced to form a double-objective optimization problem through objectives, the optimal kernel extreme learning machine parameter combination can be obtained by utilizing the optimizing capability and the faster convergence speed of a seagull optimization algorithm and combining with the multi-objective optimization strategy, and the prediction model can be established to improve the emotion tendency prediction accuracy.
Referring to fig. 4, an emotion tendency prediction method model based on kernel extreme learning machine optimization mainly comprises four modules, and each module is described as follows: the emotion tendency prediction method of the multi-target seagull optimization algorithm optimization kernel extreme learning machine comprises the steps that a data processing module is mainly responsible for processing original data and mainly comprises data cleaning, data integration, data conversion and part-of-speech tagging, and a word cloud picture is constructed. And constructing a nuclear extreme learning machine model according to the combination of the extreme learning machine theory and the extreme learning machine optimization method. Defining a multi-objective optimization strategy, and searching for an objective subset, wherein the objective subset is as small as possible, and the pareto set dominant structure of the original problem is kept or changed as little as possible. Establishing an emotion tendency prediction model by using the acquired optimal parameter set of the kernel extreme learning machine, and performing training modeling on the training data set to obtain an optimal emotion tendency prediction model; the emotion tendency prediction application module is used for applying the prediction result and comprises the output of the prediction result.
The emotion tendency prediction method for optimizing the nuclear extreme learning machine by the multi-objective seagull optimization algorithm provided by the embodiment of the invention can find the optimal parameter set of the nuclear extreme learning machine. The multi-objective function is selected as the adaptability function of the seagull optimization algorithm, the prediction result of the nuclear extreme learning machine is used as the individual adaptability value of the seagull, the main super-parameters of the nuclear extreme learning machine are optimized, and the optimal super-parameters of the nuclear extreme learning machine are found to improve the accuracy of the emotion trend prediction by utilizing the good optimizing performance and the rapid convergence rate of the multi-objective seagull optimization algorithm.
The implementation basis of the embodiments of the present invention is realized by a device with a processor function to perform programmed processing. Therefore, in engineering practice, the technical solutions and the functions of the embodiments of the present invention can be packaged into various modules. Based on the actual situation, on the basis of the above embodiments, the embodiment of the present invention provides an emotion tendency prediction device based on kernel extreme learning machine optimization, which is used for executing the emotion tendency prediction method based on kernel extreme learning machine optimization in the above method embodiment. Referring to fig. 2, the apparatus includes: the first main module is configured to implement step 1: processing an emotion tendency prediction data set, mainly comprising data cleaning, data integration, data conversion and part of speech tagging, constructing a word cloud picture, knowing word meaning and part of speech distribution, and segmenting the data set; the second main module is configured to implement step 2: the method comprises the steps that a core extreme learning machine combines a core learning theory with an optimization method of the extreme learning machine to construct a core extreme learning machine model; a third main module, configured to implement step 3: determining multi-objective function and kernel extreme learning machine optimization parameters, including initial weights and thresholds; a fourth main module, configured to implement step 4: defining a multi-target optimization strategy, and searching target subsets, wherein the target subsets keep or change the pareto set advantage structure of the original problem as little as possible; a fifth main module, configured to implement step 5: searching for a pareto solution by using a seagull optimization algorithm; a sixth main module, configured to implement step 6: judging whether the current iteration times reach the maximum iteration times Max_iter, and if so, outputting the optimal parameter combination of the multi-objective optimization kernel extreme learning machine; if not, returning to the step 5 to continue iteration; seventh main module, configured to implement step 7: establishing an emotion tendency prediction model according to the obtained optimal parameter set of the kernel extreme learning machine, and training and modeling the emotion tendency prediction model by adopting a training data set; eighth main module, configured to implement step 8: and testing the trained emotion tendency prediction model by adopting a test set, and verifying the validity of the emotion tendency prediction model.
According to the emotion tendency prediction device based on kernel extreme learning machine optimization provided by the embodiment of the invention, a plurality of modules in the figure 2 are adopted, multi-objective optimization can be carried out by integrating multi-objective scenes, the multi-objective optimization problem is reduced to form a double-objective optimization problem through objectives, the optimal kernel extreme learning machine parameter combination can be obtained by utilizing the optimizing capability and the faster convergence speed of a seagull optimization algorithm and combining with a multi-objective optimization strategy, and the prediction model can be established to improve the emotion tendency prediction accuracy.
It should be noted that, the device in the device embodiment provided by the present invention may be used to implement the method in the above method embodiment, and may also be used to implement the method in other method embodiments provided by the present invention, where the difference is merely that the corresponding functional module is provided, and the principle is basically the same as that of the above device embodiment provided by the present invention, so long as a person skilled in the art refers to a specific technical solution in the above device embodiment based on the above device embodiment, and obtains a corresponding technical means by combining technical features, and a technical solution formed by these technical means, and on the premise that the technical solution is ensured to have practicability, the device in the above device embodiment may be modified, so as to obtain a corresponding device embodiment, and be used to implement the method in other method embodiment. For example:
based on the content of the embodiment of the device, as an optional embodiment, the emotion tendency prediction device based on the optimization of the kernel extreme learning machine provided in the embodiment of the invention further includes: the first sub-module is used for realizing that the core extreme learning machine combines the core learning theory and the optimization method of the extreme learning machine to construct a core extreme learning machine model, and comprises an activation function of the core extreme learning machine:
h(x)＝k(xi,xj)
Where k (x i,xj) is a line_kernel, rbf_kernel, poly_kernel, or sigmoid_kernel type, and h (x) is an activation function of the kernel extreme learning machine model.
Based on the content of the embodiment of the device, as an optional embodiment, the emotion tendency prediction device based on the optimization of the kernel extreme learning machine provided in the embodiment of the invention further includes: the second sub-module is used for realizing that the kernel extreme learning machine combines the kernel learning theory and the optimization method of the extreme learning machine to construct a kernel extreme learning machine model, and comprises the kernel extreme learning machine model:
wherein H is an hidden layer matrix, H T is the generalized inverse of the hidden layer matrix, T is a predicted target vector, And C is a random coefficient, wherein the sum of elements and offset amounts on opposite angles in the symmetric matrix.
Based on the content of the embodiment of the device, as an optional embodiment, the emotion tendency prediction device based on the optimization of the kernel extreme learning machine provided in the embodiment of the invention further includes: a third sub-module, configured to implement the multiple objective functions in step 3, including:
Wherein obf 1 is a mean square error, obf 2 is a standard deviation, N represents the number of samples, y i represents the ith predicted value, y i represents the ith predicted value, min is the minimum value, and N is the number of predicted values.
Based on the content of the embodiment of the device, as an optional embodiment, the emotion tendency prediction device based on the optimization of the kernel extreme learning machine provided in the embodiment of the invention further includes: a fourth sub-module, configured to implement the pareto set dominance structure that retains or changes the original problem as little as possible for the target subset in step 4, including: defining an external archive for storing the optimal non-dominant pareto solution, determining whether to classify a solution as an optimal non-dominant pareto solution; the nearest neighbor heuristic takes the individual closest to the current individual as the next individual.
Based on the content of the embodiment of the device, as an optional embodiment, the emotion tendency prediction device based on the optimization of the kernel extreme learning machine provided in the embodiment of the invention further includes: a fifth sub-module, configured to implement an index function, including:
where F ij (μ) represents the distance between individuals i and j, μ represents the weight coefficient, the search distance and time are balanced, F represents the transposed matrix, d ij represents the distance between two points i and j, and V represents the target space.
Based on the content of the embodiment of the device, as an optional embodiment, the emotion tendency prediction device based on the optimization of the kernel extreme learning machine provided in the embodiment of the invention further includes: and a sixth sub-module, configured to implement the searching for pareto solutions using a seagull optimization algorithm, including: the attack and migration of seagulls are utilized to find out the optimal population individuals and the positions; substituting the optimal target optimization function to find the optimal non-dominant pareto solution.
The method of the embodiment of the invention is realized by the electronic equipment, so that the related electronic equipment is necessary to be introduced. To this end, an embodiment of the present invention provides an electronic device, as shown in fig. 3, including: at least one processor (processor), a communication interface (Communications Interface), at least one memory (memory), and a communication bus, wherein the at least one processor, the communication interface, and the at least one memory communicate with each other via the communication bus. The at least one processor may invoke logic instructions in the at least one memory to perform all or part of the steps of the methods provided by the various method embodiments described above.
Further, the logic instructions in at least one of the memories described above may be implemented in the form of a software functional unit and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or may be implemented by hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. Based on this knowledge, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the present disclosure, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. The emotion tendency prediction method based on the optimization of the kernel extreme learning machine is characterized by comprising the following steps of: step 1: processing the emotion tendency prediction data set, including data cleaning, data integration, data conversion and part of speech tagging, constructing a word cloud picture, knowing word meaning and part of speech distribution, and segmenting the data set; step 2: the method comprises the steps that a core extreme learning machine combines a core learning theory with an optimization method of the extreme learning machine to construct a core extreme learning machine model; step 3: determining multi-objective function and kernel extreme learning machine optimization parameters, including initial weights and thresholds; step 4: defining a multi-target optimization strategy, and searching target subsets, wherein the target subsets keep or change the pareto set advantage structure of the original problem as little as possible; step 5: searching for a pareto solution by using a seagull optimization algorithm; step 6: judging whether the current iteration times reach the maximum iteration times Max_iter, and if so, outputting the optimal parameter combination of the multi-objective optimization kernel extreme learning machine; if not, returning to the step 5 to continue iteration; step 7: establishing an emotion tendency prediction model according to the obtained optimal parameter set of the kernel extreme learning machine, and training and modeling the emotion tendency prediction model by adopting a training data set; step 8: testing the trained emotion tendency prediction model by adopting a test set, and verifying the validity of the emotion tendency prediction model;
the core extreme learning machine combines the core learning theory and the optimization method of the extreme learning machine to construct a core extreme learning machine model, and the core extreme learning machine model comprises an activation function of the core extreme learning machine:
h(x)＝k(xi,xj)
Wherein k (x i,xj) is a line_kernel, RBF_kernel, poly_kernel or sigmoid_kernel type, and h (x) is an activation function of the kernel extreme learning machine model;
the nuclear extreme learning machine combines a nuclear learning theory and an optimization method of the extreme learning machine to construct a nuclear extreme learning machine model, wherein the nuclear extreme learning machine model comprises the following components:
wherein H is an hidden layer matrix, H T is the generalized inverse of the hidden layer matrix, T is a predicted target vector, The sum of elements and offset on opposite angles in the symmetrical matrix is given, and C is a random coefficient;
The target subset in step 4 retains or changes the pareto set dominance structure of the original problem as little as possible, comprising: defining an external archive for storing the optimal non-dominant pareto solution, determining whether to classify a solution as an optimal non-dominant pareto solution; the nearest neighbor heuristic algorithm takes the individual closest to the current individual as the next individual;
the objective function includes:
Wherein f ij (μ) represents the distance between individuals i to j, μ represents the weight coefficient, balancing the search distance and time, Representing the transposed matrix, d ij represents the distance between points i and j, and V represents the target space.
2. The emotion trend prediction method based on kernel extreme learning machine optimization of claim 1, wherein the multi-objective function in step 3 comprises:
Wherein obf 1 is the mean square error, obf 2 is the standard deviation, N represents the number of samples, Representing the ith predicted value, y i represents the ith predicted value, min is the minimum value, and N is the number of predicted values.
3. The emotion tendency prediction method based on kernel extreme learning machine optimization of claim 1, wherein the searching for the pareto solution by using a seagull optimization algorithm comprises: the attack and migration of seagulls are utilized to find out the optimal population individuals and the positions; substituting the optimal target optimization function to find the optimal non-dominant pareto solution.
4. An emotion tendency prediction device based on kernel extreme learning machine optimization, which is characterized by comprising: the first main module is configured to implement step 1: processing the emotion tendency prediction data set, including data cleaning, data integration, data conversion and part of speech tagging, constructing a word cloud picture, knowing word meaning and part of speech distribution, and segmenting the data set; the second main module is configured to implement step 2: the method comprises the steps that a core extreme learning machine combines a core learning theory with an optimization method of the extreme learning machine to construct a core extreme learning machine model; a third main module, configured to implement step 3: determining multi-objective function and kernel extreme learning machine optimization parameters, including initial weights and thresholds; a fourth main module, configured to implement step 4: defining a multi-target optimization strategy, and searching target subsets, wherein the target subsets keep or change the pareto set advantage structure of the original problem as little as possible; a fifth main module, configured to implement step 5: searching for a pareto solution by using a seagull optimization algorithm; a sixth main module, configured to implement step 6: judging whether the current iteration times reach the maximum iteration times Max_iter, and if so, outputting the optimal parameter combination of the multi-objective optimization kernel extreme learning machine; if not, returning to the step 5 to continue iteration; seventh main module, configured to implement step 7: establishing an emotion tendency prediction model according to the obtained optimal parameter set of the kernel extreme learning machine, and training and modeling the emotion tendency prediction model by adopting a training data set; eighth main module, configured to implement step 8: testing the trained emotion tendency prediction model by adopting a test set, and verifying the validity of the emotion tendency prediction model;
the core extreme learning machine combines the core learning theory and the optimization method of the extreme learning machine to construct a core extreme learning machine model, and the core extreme learning machine model comprises an activation function of the core extreme learning machine:
h(x)＝k(xi,xj)
Wherein k (x i,xj) is a line_kernel, RBF_kernel, poly_kernel or sigmoid_kernel type, and h (x) is an activation function of the kernel extreme learning machine model;
the nuclear extreme learning machine combines a nuclear learning theory and an optimization method of the extreme learning machine to construct a nuclear extreme learning machine model, wherein the nuclear extreme learning machine model comprises the following components:
wherein H is an hidden layer matrix, H T is the generalized inverse of the hidden layer matrix, T is a predicted target vector, The sum of elements and offset on opposite angles in the symmetrical matrix is given, and C is a random coefficient;
The target subset in step 4 retains or changes the pareto set dominance structure of the original problem as little as possible, comprising: defining an external archive for storing the optimal non-dominant pareto solution, determining whether to classify a solution as an optimal non-dominant pareto solution; the nearest neighbor heuristic algorithm takes the individual closest to the current individual as the next individual;
the objective function includes:
Wherein f ij (μ) represents the distance between individuals i to j, μ represents the weight coefficient, balancing the search distance and time, Representing the transposed matrix, d ij represents the distance between points i and j, and V represents the target space.
5. An electronic device, comprising:
At least one processor, at least one memory, and a communication interface; wherein,
The processor, the memory and the communication interface are communicated with each other;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-3.
6. A non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the method of any one of claims 1 to 3.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108428017A (en) * | 2018-04-23 | 2018-08-21 | 华北电力大学 | Wind power interval prediction method based on core extreme learning machine quantile estimate |
| CN111695611A (en) * | 2020-05-27 | 2020-09-22 | 电子科技大学 | Bee colony optimization kernel extreme learning and sparse representation mechanical fault identification method |
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