A Novel Heuristic Artificial Neural Network Model for Urban Computing
Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. After decades of rapid development, artificial neural networks (ANN) have been successfully applied in many disciplines and have enabled many remarkable...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8936976/ |
| _version_ | 1818875583341264896 |
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| author | Qi Na Guisheng Yin Ang Liu |
| author_facet | Qi Na Guisheng Yin Ang Liu |
| author_sort | Qi Na |
| collection | DOAJ |
| description | Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. After decades of rapid development, artificial neural networks (ANN) have been successfully applied in many disciplines and have enabled many remarkable research achievements. However, no quantitative method has yet been found that can identify every parameter to optimize a neural network. The BP neural network is most frequently used but suffers from the following defects with respect to complex and multidimensional training data or setting of different parameters, i.e., overfitting, slow convergence speed, trapping in local optima and poor prediction effect, and these obstacles have greatly restricted its practical applications. Therefore, this paper proposes a method that uses ant colony optimization (ACO) to train the parameters and structure of the neural network, optimizes its weight and threshold to solve its defects, and applies the model in the optimization scheme of urban operation and management to verify its effect. The experimental simulation proves that the method in this paper is effective and that it makes certain improvements in the local and global search ability, speed, and accuracy of the neural network. |
| first_indexed | 2024-12-19T13:28:48Z |
| format | Article |
| id | doaj.art-a51e044291914d9ab246ba2ba088c093 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:28:48Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a51e044291914d9ab246ba2ba088c0932022-12-21T20:19:29ZengIEEEIEEE Access2169-35362019-01-01718375118376010.1109/ACCESS.2019.29606878936976A Novel Heuristic Artificial Neural Network Model for Urban ComputingQi Na0https://orcid.org/0000-0001-7591-4892Guisheng Yin1https://orcid.org/0000-0003-0924-4741Ang Liu2https://orcid.org/0000-0003-0749-1749Centre for Big Data and Business Intelligence, Harbin Engineering University, Harbin, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin, ChinaSchool of Management, Heilongjiang University of Science and Technology, Harbin, ChinaUrban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. After decades of rapid development, artificial neural networks (ANN) have been successfully applied in many disciplines and have enabled many remarkable research achievements. However, no quantitative method has yet been found that can identify every parameter to optimize a neural network. The BP neural network is most frequently used but suffers from the following defects with respect to complex and multidimensional training data or setting of different parameters, i.e., overfitting, slow convergence speed, trapping in local optima and poor prediction effect, and these obstacles have greatly restricted its practical applications. Therefore, this paper proposes a method that uses ant colony optimization (ACO) to train the parameters and structure of the neural network, optimizes its weight and threshold to solve its defects, and applies the model in the optimization scheme of urban operation and management to verify its effect. The experimental simulation proves that the method in this paper is effective and that it makes certain improvements in the local and global search ability, speed, and accuracy of the neural network.https://ieeexplore.ieee.org/document/8936976/Urban computingartificial neural network modelstructure and parameterant colony optimizationspeed and accuracy |
| spellingShingle | Qi Na Guisheng Yin Ang Liu A Novel Heuristic Artificial Neural Network Model for Urban Computing IEEE Access Urban computing artificial neural network model structure and parameter ant colony optimization speed and accuracy |
| title | A Novel Heuristic Artificial Neural Network Model for Urban Computing |
| title_full | A Novel Heuristic Artificial Neural Network Model for Urban Computing |
| title_fullStr | A Novel Heuristic Artificial Neural Network Model for Urban Computing |
| title_full_unstemmed | A Novel Heuristic Artificial Neural Network Model for Urban Computing |
| title_short | A Novel Heuristic Artificial Neural Network Model for Urban Computing |
| title_sort | novel heuristic artificial neural network model for urban computing |
| topic | Urban computing artificial neural network model structure and parameter ant colony optimization speed and accuracy |
| url | https://ieeexplore.ieee.org/document/8936976/ |
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