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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1550-1477 |