A stochastic artificial neural network model for investigating street vendor behavior in a night market
This article offers a hybrid computational approach that combines an artificial neural network with Bayesian probability to improve on the conventional artificial neural network model. The artificial neural network model, which is renowned for its pattern classification abilities, is a type of deter...
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| Format: | Article |
| Language: | English |
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Wiley
2016-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147716673371 |
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| _version_ | 1832559173678661632 |
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| author | Pao-Kuan Wu Tsung-Chih Hsiao Ming Xiao |
| author_facet | Pao-Kuan Wu Tsung-Chih Hsiao Ming Xiao |
| author_sort | Pao-Kuan Wu |
| collection | DOAJ |
| description | This article offers a hybrid computational approach that combines an artificial neural network with Bayesian probability to improve on the conventional artificial neural network model. The artificial neural network model, which is renowned for its pattern classification abilities, is a type of deterministic algorithm. However, combining artificial neural network with Bayesian probability can convert the deterministic artificial neural network model into a stochastic artificial neural network model that is useful for conducting dynamic simulations. In this study, an experiment is performed to demonstrate this hybrid computational approach. The objective of this experiment is to analyze the behavior of illegal street vendors in a night market. By applying the hybrid computational approach, we can perform a series of dynamic simulations to investigate the development process of the illegal street vendors. The results of the dynamic simulation have high similarity with the real observations. Furthermore, we can use the simulation results to evaluate the commercial values of different parts of streets and to determine which streets will be unstable due to the impacts of economic fluctuations. |
| format | Article |
| id | doaj-art-da95a17bd09e4866a2cb66c8cbcfdd07 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-da95a17bd09e4866a2cb66c8cbcfdd072025-02-03T01:30:41ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-10-011210.1177/1550147716673371A stochastic artificial neural network model for investigating street vendor behavior in a night marketPao-Kuan Wu0Tsung-Chih Hsiao1Ming Xiao2Department of Urban Planning, College of Architecture, Huaqiao University, Xiamen, ChinaCollege of Computer Science and Technology, Huaqiao University, Quanzhou, ChinaDepartment of Urban Planning, College of Architecture, Huaqiao University, Xiamen, ChinaThis article offers a hybrid computational approach that combines an artificial neural network with Bayesian probability to improve on the conventional artificial neural network model. The artificial neural network model, which is renowned for its pattern classification abilities, is a type of deterministic algorithm. However, combining artificial neural network with Bayesian probability can convert the deterministic artificial neural network model into a stochastic artificial neural network model that is useful for conducting dynamic simulations. In this study, an experiment is performed to demonstrate this hybrid computational approach. The objective of this experiment is to analyze the behavior of illegal street vendors in a night market. By applying the hybrid computational approach, we can perform a series of dynamic simulations to investigate the development process of the illegal street vendors. The results of the dynamic simulation have high similarity with the real observations. Furthermore, we can use the simulation results to evaluate the commercial values of different parts of streets and to determine which streets will be unstable due to the impacts of economic fluctuations.https://doi.org/10.1177/1550147716673371 |
| spellingShingle | Pao-Kuan Wu Tsung-Chih Hsiao Ming Xiao A stochastic artificial neural network model for investigating street vendor behavior in a night market International Journal of Distributed Sensor Networks |
| title | A stochastic artificial neural network model for investigating street vendor behavior in a night market |
| title_full | A stochastic artificial neural network model for investigating street vendor behavior in a night market |
| title_fullStr | A stochastic artificial neural network model for investigating street vendor behavior in a night market |
| title_full_unstemmed | A stochastic artificial neural network model for investigating street vendor behavior in a night market |
| title_short | A stochastic artificial neural network model for investigating street vendor behavior in a night market |
| title_sort | stochastic artificial neural network model for investigating street vendor behavior in a night market |
| url | https://doi.org/10.1177/1550147716673371 |
| work_keys_str_mv | AT paokuanwu astochasticartificialneuralnetworkmodelforinvestigatingstreetvendorbehaviorinanightmarket AT tsungchihhsiao astochasticartificialneuralnetworkmodelforinvestigatingstreetvendorbehaviorinanightmarket AT mingxiao astochasticartificialneuralnetworkmodelforinvestigatingstreetvendorbehaviorinanightmarket AT paokuanwu stochasticartificialneuralnetworkmodelforinvestigatingstreetvendorbehaviorinanightmarket AT tsungchihhsiao stochasticartificialneuralnetworkmodelforinvestigatingstreetvendorbehaviorinanightmarket AT mingxiao stochasticartificialneuralnetworkmodelforinvestigatingstreetvendorbehaviorinanightmarket |