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首页> 外文期刊>Journal of computational and theoretical nanoscience >Prediction of Environmental Carrying Capacity for Chinese Cities Based on Artificial Neural Network and Grey Model GM (1, 1) Prediction
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Prediction of Environmental Carrying Capacity for Chinese Cities Based on Artificial Neural Network and Grey Model GM (1, 1) Prediction

机译：基于人工神经网络和灰色模型GM（1,1）预测的中国城市环境承载能力预测

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摘要

Environmental carrying capacity of Chinese cities is of great significance in the social prediction and social management. The change regulation of the environmental carrying capacity in Chinese cities is various from year to year. Previous research use the multiple linear regression(MLR) method to develop a series of prediction models for the prediction of environmental carrying capacity. However, results are not accurate enough. It is highly difficult to measure the impacts of different independent variables because of the randomness of social science. Therefore, theMLR approach may not quite suitable for adapting the actual applications. Here, we present two strong models in order to improve the prediction precision of the environmental carrying capacity of Chinese cities. Artificial neural networks (ANNs) and grey model GM (1, 1) prediction is proposed.We use general regression neural network (GRNN) and multi-layer feed-forward neural network (MLFN) models as the trained ANN models. Best net search approach is used for searching the most suitable ANN model according to the RMSE and training time. GM (1, 1) model is proposed using the numbersequence of the year, which is dependent to various independent variables. Results show that both the ANNs and GM (1, 1) can be effectively used for the prediction of the environmental carrying capacity of Chinese cities. Under different conditions can we decide to use a certain suitable predictionmodel.
机译：中国城市的环境承载能力在社会预测和社会管理方面具有重要意义。中国城市环境承载能力的变化规范是多年来的各种。以前的研究使用多元线性回归（MLR）方法开发一系列预测模型，用于预测环境承载能力。但是，结果不够准确。由于社会科学的随机性，衡量不同独立变量的影响很难。因此，TheMLR方法可能不太适合调整实际应用。在这里，我们提出了两个强大的模型，以提高中国城市环境承载力的预测精度。提出了人工神经网络（ANNS）和灰色模型GM（1,1）预测。我们使用一般回归神经网络（GRNN）和多层前馈神经网络（MLFN）模型作为培训的ANN型号。最好的净搜索方法用于根据RMSE和培训时间搜索最合适的ANN模型。使用年度的Numbereequence提出了GM（1,1）模型，这取决于各种独立变量。结果表明，ANNS和GM（1,1）都可以有效地用于预测中国城市的环境承载力。在不同的条件下，我们可以决定使用某个合适的预测模型。

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