Abstract
BP neural network method is provided by the outstanding characteristics of self-learning and non-linearity, and can obtain relatively satisfactory prediction results, which also can be used to forecast innovation output. The neural network toolbox function of Matlab can build a neural network prediction model to predict the innovation output from 2008 to 2017. Second, the dynamic SDM is used to empirically test the role of industrial cluster on the innovation efficiency and its space spillover effect by using of the panel data of Chinese cities. The results show the error comparison between the predicted value and real value of innovation efficiency, which explains the accuracy of BP neural network is higher. There is a spatial distribution pattern in which the innovation efficiency decreases from the east, the middle, and the west, which also has the characteristic of time inertia and positive spatial correlation. The producer service agglomeration has significantly improved the innovation efficiency in this city but has no significant role on the innovation efficiency in neighboring cities. The manufacturing cluster has a significant negative effect on the innovation efficiency in this city in the long and short term but produces a significant positive effect on innovation efficiency in neighboring cities in the long and short term.
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