A stochastic artificial neural network model for investigating street vendor behavior in a night market

Pao-Kuan Wu, Tsung-Chih Hsiao and Ming Xiao

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 10, 1550147716673371

Abstract: 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.

Keywords: Artificial neural network; Bayesian probability; geographic information system; dynamic simulation (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:10:p:1550147716673371

DOI: 10.1177/1550147716673371

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