The spatial pattern and evolution of urban system have been hot research issues in the field of urban research. In this paper, the network analysis method based on the gravity model and the related measurements were used to reveal the properties of the spatial pattern and evolution of the urban system in the HRB (Huaihe River Basin) of China. The findings of this study are as follows: During the period from 2006 to 2014, the economic contact between the HRB cities has been strengthened, but the differences between cities have been expanding. In general, the HRB cities have not yet formed a close network structure, and a trend of economic integration has not been found. This paper expresses the spatial pattern and evolution of urban system in an intuitive way and helps to explain the evolution mechanism of urban system. The method was confirmed by empirical research. Because of the operational and visual expression, this method has broad application prospects in the urban system research.
In order to increase the accuracy of the cost estimates in government investment, a method based on the PSO trained neural network to estimate the cost is proposed. First the neural network model of a project cost estimate is created, and then PSO is introduced to optimize the weight and threshold of the neural network, at last the neural network trained is used to estimate cost of the project. The results show that this method not only overcome the defects of traditional nueral network such as learning for a long time and shock, but also increase the accuracy of the cost of the project to estimate.
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