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Terrorist attacks change dynamically in social and geographic spaces. In this paper, terrorist attacks in the Middle East are analyzed using methods of network science, statistical methods, geographic information science, and artificial neural networks designed from a socio-spatial perspective. Based on the Global Terrorism Database (GTD), firstly the distribution and trends of terrorist attacks are...
In this paper, we construct a simple data-driven trend tracking strategy for gold future in a view of contrarians. The artificial neutral network (ANN) is adopted to determine the price trend signal, by which the degree of tightness could be adjusted based on observed data. We attempt to capture the small profits when the price is deviated from the Bollinger band in the gold future market by intraday...
In terms of the Particle Swarm Optimization-Neural Network (PSO-NN), a new prediction model has been developed using the stepwise regression method combined with the feature extraction technique of Isometric Mapping (ISOMAP) algorithm to treat the Climatology and Persistence (CLIPER) predictors. The model is validated with forecasts of ten years of typhoon intensity formed and numbered in the Western...
In HVDC systems, power converters are well known the generators of harmonics on both ac and dc side. Because of the interaction between ac and dc systems, some kinds of disturbances in the ac or dc side may cause the fundamental or second harmonic current on the dc side. When the dc loop of the HVDC system has natural resonant frequencies on the fundamental or the second harmonic frequencies, the...
A new calculation method for the input of the neural network ensemble prediction (NNEP) model has been developed based on the data mining technology using the feature extraction method of Empirical Orthogonal Function(EOF) and the stepwise regression method, for investigating the effect of different model input with the same dimension on the prediction capacity of the NNEP model. Taking typhoon intensity...
Taking the mean precipitation from 16 stations spread around the south China during the pre-flood season as the prediction object treated by Empirical Orthogonal Function (EOF) method, previous physical predictors and factors that reflected the significant period of predictands by means of the Mean Generating Functions (MGF) technique, were extracted useful information for prediction by using Partial...
Most algorithms developed for encryption and decryption on network information security are focused on logic analysis. However, they are complicated to implement as a system and are difficult to be applied in a widespread manner. Recently, biomimetic-based architecture of artificial neural network was proposed to improve the reliability and performance of encryption methods such as back-propagation...
This study reveals the properties of the input/output relationship for a real-valued single-hidden layer feed-forward neural network (SLFN) with the tanh activation function on all hidden-layer nodes and the linear activation function on output node. Specifically, the rule-extraction of the SLFN is done through mathematically analyzing its preimage, which is the set of input values for a given output...
To improve the predictive ability of a fuzzy neural network prediction model, the re-selection is made, by means the rough set attribute reduction, of the correlated prognostic factors that have been chosen and the re-selected factors are treated by blurring as model input, thereby establishing a new-type fuzzy neural network predictive model. Experiments are conducted for approximately two months...
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