文章基本信息

标题：Distance Prediction for Commercial Serial Crime Cases Using Time Delay Neural Networks
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作者：Anahita Ghazvini ; Siti Norul Huda Sheikh Abdullah ; Mohammed Ariff Abdullah 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2015
卷号：6
期号：11
DOI：10.14569/IJACSA.2015.061101
出版社：Science and Information Society (SAI)
摘要：The prediction of the next serial criminal time is important in the field of criminology for preventing the recurring actions of serial criminals. In the associated dynamic systems, one of the main sources of instability and poor performances is the time delay, which is commonly predicted based on nonlinear methods. The aim of this study is to introduce a dynamic neural network model by using nonlinear autoregressive time series with exogenous (external) input (NARX) and Back Propagation Through Time (BPTT), which is verified intensively with MATLAB to predict and model the crime times for the next distance of serial cases. Recurrent neural networks have been extensively used for modeling of nonlinear dynamic systems. There are different types of recurrent neural networks such as Time Delay Neural Networks (TDNN), layer recurrent networks, NARX, and BPTT. The NARX model for the two cases of input- output modeling of dynamic systems and time series prediction draw more attention. In this study, a comparison of two models of NARX and BPTT used for the prediction of the next serial criminal time illustrates that the NARX model exhibits better performance for the prediction of serial cases than the BPTT model. Our future work aims to improve the NARX model by combining objective functions.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Criminology and Computational Criminology; Neural Network; modeling; NARX; BPTT; Quantum GIS