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摘要: 用神经网络法预测垃圾热值是利用当地经济和自然环境统计数据进行热值估算的方法。灰色关联度分析得出燃气普及率、年降雨量、城镇居民人均可支配收入、地区生产总值等是影响城市生活垃圾热值的主要因素,将这些影响因素作为神经网络的输入参数,结合多年对成都市垃圾热值的跟踪检测结果,利用MATLAB工具箱建立了基于社会经济统计数据的城市生活垃圾热值BP神经网络估算模型。开集和闭集测试结果表明,该模型仿真效果好,计算准确度高,仿真结果误差小,网络稳定性好,训练速度快,能够有效的进行仿真计算。Abstract: The neural network method to forecast the heating value of Municipal Solid Waste (MSW) is a new way to estimate heating value of MSW by using statistics of economy and natural environment. Indicated by Grey Correlation Coefficient, there are four key factors that affect the heating value of MSW, including rate of gas utilization, annual rainfall, per capita annual living expenditures of urban households and GDP.A social-economic statistics based BP neural network estimation model of the heating value of MSW was established, by using MATLAB with the statistics of those key factors and the historical tracking results of heating value of MSW from Chendu City. The closed set and open set test show that the model has a high precision and stabilization. It can do simulation effectively with data outside of the training sample and can perform fast training.
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Keywords:
- municipal solid /
- heat value /
- level of economic development /
- grey relational analysis /
- neural network /
- model
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