Application of Mathematical Economic Model in Financial System in Manufacturing Industry
In the field of economic research, most of the sample data is not obtained based on controllable experiments but generated during the normal operation of the economic system. Therefore, the change of an economic variable is usually not caused by a single change of a cause variable. It is the result...
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          | Published in | Wireless communications and mobile computing Vol. 2021; no. 1 | 
|---|---|
| Main Author | |
| Format | Journal Article | 
| Language | English | 
| Published | 
        Oxford
          Hindawi
    
        2021
     John Wiley & Sons, Inc  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1530-8669 1530-8677 1530-8677  | 
| DOI | 10.1155/2021/2440896 | 
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| Abstract | In the field of economic research, most of the sample data is not obtained based on controllable experiments but generated during the normal operation of the economic system. Therefore, the change of an economic variable is usually not caused by a single change of a cause variable. It is the result of a combination of multiple factors. Therefore, it is necessary to study the application of mathematical intelligent computing in computer intelligent manufacturing system. The purpose of this paper is to explore the application of mathematical intelligent computing in computer intelligent manufacturing system. For this reason, this paper uses the furnace temperature control model to carry out simulation experiment. In this simulation experiment, three algorithms of mathematical intelligent computing are mainly used, including BPES intelligent computing method, genetic algorithm, and MARS algorithm. The research results show that the superparameter optimization based on MARS has high efficiency, and the best result, the worst result, the average result, the variance, and the average time of multiple independent runs are controlled below 0.03 s. In this experiment, when the hidden layer node is 9, the prediction error value is the smallest, and the model simulation curve is basically consistent with the measured curve trend. In the simulation experiment of this paper, these three algorithms have shown good results in their respective links. | 
    
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| AbstractList | In the field of economic research, most of the sample data is not obtained based on controllable experiments but generated during the normal operation of the economic system. Therefore, the change of an economic variable is usually not caused by a single change of a cause variable. It is the result of a combination of multiple factors. Therefore, it is necessary to study the application of mathematical intelligent computing in computer intelligent manufacturing system. The purpose of this paper is to explore the application of mathematical intelligent computing in computer intelligent manufacturing system. For this reason, this paper uses the furnace temperature control model to carry out simulation experiment. In this simulation experiment, three algorithms of mathematical intelligent computing are mainly used, including BPES intelligent computing method, genetic algorithm, and MARS algorithm. The research results show that the superparameter optimization based on MARS has high efficiency, and the best result, the worst result, the average result, the variance, and the average time of multiple independent runs are controlled below 0.03 s. In this experiment, when the hidden layer node is 9, the prediction error value is the smallest, and the model simulation curve is basically consistent with the measured curve trend. In the simulation experiment of this paper, these three algorithms have shown good results in their respective links. | 
    
| Author | Tang, Boqi | 
    
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| CitedBy_id | crossref_primary_10_1080_0951192X_2024_2318730 crossref_primary_10_2478_amns_2024_1187  | 
    
| Cites_doi | 10.1016/j.ifacol.2015.06.358 10.20965/ijat.2018.p0282 10.1520/SSMS20160012 10.1631/FITEE.1900094 10.4236/ijcns.2017.103003 10.1016/j.ifacol.2015.06.321 10.1016/j.eng.2019.01.014 10.18226/23185279.v7iss2p126 10.1007/s40684-020-00227-1 10.17485/ijst/2015/v8i35/86698 10.4018/IJWSR.2017070102 10.1002/cae.21849 10.1007/s12293-019-00297-y 10.20965/ijat.2017.p0004 10.1016/j.ifacol.2017.08.2548 10.1177/0954405417736547 10.1016/j.ifacol.2017.08.2349 10.1007/s00170-020-05124-0 10.1109/LRA.2017.2714135 10.1007/s12652-018-1073-z 10.1016/j.ifacol.2019.06.123 10.1177/0954405419842412 10.1007/s11042-017-4445-z 10.1038/544023a 10.3745/JIPS.04.0103  | 
    
| ContentType | Journal Article | 
    
| Copyright | Copyright © 2021 Boqi Tang. Copyright © 2021 Boqi Tang. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.  | 
    
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| SubjectTerms | Back propagation Bioinformatics Chromosomes Computation Economic analysis Economic models Evolution Genetic algorithms Intelligent manufacturing systems Interdisciplinary subjects Manufacturing Mathematical analysis Mutation Neural networks Neurons Optimization Optimization algorithms Population Propagation Simulation Stability Temperature control Traveling salesman problem  | 
    
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| Title | Application of Mathematical Economic Model in Financial System in Manufacturing Industry | 
    
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