Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness
This paper researches and analyzes the evaluation of the competitiveness of ice and snow tourism, uses the improved fuzzy neural network algorithm to process the system flow diagram of ice and snow tourism development through the function and characteristics of the power system of ice and snow touri...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9980528 |
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| _version_ | 1832554105891979264 |
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| author | Ying Zhao Qinghua Zhu Jiujun Bai |
| author_facet | Ying Zhao Qinghua Zhu Jiujun Bai |
| author_sort | Ying Zhao |
| collection | DOAJ |
| description | This paper researches and analyzes the evaluation of the competitiveness of ice and snow tourism, uses the improved fuzzy neural network algorithm to process the system flow diagram of ice and snow tourism development through the function and characteristics of the power system of ice and snow tourism, and finally selects more than 40 indicators of the three subsystems of resources, economy, and culture. Based on the construction of cloud fuzzy neural network model, the above method is used for experimental comparison analysis, and experiments are conducted through University of California Irvine (UCI) dataset and engineering examples to compare with the traditional cloud model, fuzzy neural network, and BP neural network to analyze the operation efficiency, accuracy rate, and several rules of the algorithm. Through the experimental comparative analysis, the cloud fuzzy neural network can fully take into account the randomness and fuzziness of the data, optimize the generation of cloud rules, avoid multidimensional rule disasters, and ensure the operational efficiency of the algorithm; the accuracy rate of the algorithm is improved relative to that of the traditional technology, and it applies to a variety of datasets. And the software is used to test the ice and snow tourism industry system dynamics model to realize the correctness and robustness testing of the model. After the constructed model can reflect the real situation within the error range, the final policy simulation of the model is carried out. |
| format | Article |
| id | doaj-art-a61c1acabeeb4540b05b54d129ea3bce |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a61c1acabeeb4540b05b54d129ea3bce2025-02-03T05:52:26ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99805289980528Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism CompetitivenessYing Zhao0Qinghua Zhu1Jiujun Bai2School of Economics and Management, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian, Liaoning Province 116024, ChinaAntai College of Economics and Management, Shanghai Jiaotong University, No. 1954 Huashan Road, Shanghai 200030, ChinaUniversity of Science and Technology Liaoning, No. 189 Qianshan Road, Anshan, Liaoning Province 114051, ChinaThis paper researches and analyzes the evaluation of the competitiveness of ice and snow tourism, uses the improved fuzzy neural network algorithm to process the system flow diagram of ice and snow tourism development through the function and characteristics of the power system of ice and snow tourism, and finally selects more than 40 indicators of the three subsystems of resources, economy, and culture. Based on the construction of cloud fuzzy neural network model, the above method is used for experimental comparison analysis, and experiments are conducted through University of California Irvine (UCI) dataset and engineering examples to compare with the traditional cloud model, fuzzy neural network, and BP neural network to analyze the operation efficiency, accuracy rate, and several rules of the algorithm. Through the experimental comparative analysis, the cloud fuzzy neural network can fully take into account the randomness and fuzziness of the data, optimize the generation of cloud rules, avoid multidimensional rule disasters, and ensure the operational efficiency of the algorithm; the accuracy rate of the algorithm is improved relative to that of the traditional technology, and it applies to a variety of datasets. And the software is used to test the ice and snow tourism industry system dynamics model to realize the correctness and robustness testing of the model. After the constructed model can reflect the real situation within the error range, the final policy simulation of the model is carried out.http://dx.doi.org/10.1155/2021/9980528 |
| spellingShingle | Ying Zhao Qinghua Zhu Jiujun Bai Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness Complexity |
| title | Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness |
| title_full | Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness |
| title_fullStr | Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness |
| title_full_unstemmed | Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness |
| title_short | Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness |
| title_sort | fuzzy neural network based evaluation algorithm for ice and snow tourism competitiveness |
| url | http://dx.doi.org/10.1155/2021/9980528 |
| work_keys_str_mv | AT yingzhao fuzzyneuralnetworkbasedevaluationalgorithmforiceandsnowtourismcompetitiveness AT qinghuazhu fuzzyneuralnetworkbasedevaluationalgorithmforiceandsnowtourismcompetitiveness AT jiujunbai fuzzyneuralnetworkbasedevaluationalgorithmforiceandsnowtourismcompetitiveness |