In the empirical researches, the discrete GM (1,1) model is not always fitted well, and sometimes the forecasting error is large. In order to solve this issue, this study proposes a dynamic discrete GM (1,1) model based on the grey prediction theory and the GM (1,1) model. In this paper, we use the equal division technology to fit the concavity and convexity of the cumulative sequence and then construct two dynamic average values. Based on the dynamic average values, we further develop two dynamic discrete GM (1,1) models and provide the gradual heuristics method to draw the initial equal division number and the dichotomy approach to optimize the equal division number. Finally, based on an empirical analysis of the number of conflict events in the urbanization process in China, we verify that the dynamic discrete GM (1,1) model has higher fitting and prediction accuracy than the GM (1,1) model and the discrete GM (1,1) model, and its prediction result is beneficial to the government for prevention and solution of the urbanization conflict events.
China and Laos have a very long tradition of trade, in recent years, with the "The Belt and Road initiative" is proposed, the Chinese enterprise investment in Laos has entered a new period of development. At the same time, the development of transportation infrastructure is very poor, which seriously restricts the overall economic development of Laos. In this paper, the heat analysis method is used to analyze the investment environment of transportation infrastructure in Laos, which provides a guiding role for the investment of Chinese enterprises in Laos.
The traditional method of electronic nose (E-nose) data processing has the disadvantages of cumbersome operation steps and low classification accuracy. To address these problems, this paper proposes a convolutional spiking neural network (CSNN) for E-nose data processing that combines residual blocks. The network model consists of spiking-convolutional layers and fully connected pulse layers. The model combines the feature extraction capability of a convolutional neural network (CNN) with the computational efficiency of a spiking neural network (SNN) and the good biointerpretability of spike signal data and makes use of residual blocks to allow the network to learn richer content. In addition, two spike coding methods (response rate coding and response value coding) are designed to encode the data to make great use of the sensor curve features. To test the performance of the proposed network model in the E-nose, the data collected by the self-built E-nose system were used to identify and classify ten toxic gases with a maximum classification rate of 96.39%.
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