| 基于人工神经网络的居民生活垃圾可燃成分热值预测 |
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| 引用本文： | 丁兰, 张文阳, 张良均, 陈俊德. 基于人工神经网络的居民生活垃圾可燃成分热值预测[J]. 环境工程学报, 2016, 10(2): 899-905. doi: 10.12030/j.cjee.20160261 |
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| 作者姓名： | 丁兰 张文阳 张良均 陈俊德 |
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| 作者单位： | 1.西南交通大学地球科学与环境工程学院, 成都 611756; 2.广州泰迪智能科技有限公司, 广州 510665 |
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| 基金项目： | 四川省科技支撑项目(2009SZ0211) |
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| 摘 要： | 研究采用BP、RBF和自适应神经模糊推理系统(ANFIS)对生活垃圾可燃成分的热值进行预测。结果表明,BP神经网络模型的预测准确率为93.36%,RBF模型为96.87%,ANFIS模型为91.06%,3种模型均可用于可燃成分热值预测,但RBF模型的预测准确率相对较高,更适用于可燃垃圾的热值预测。
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| 关 键 词： | 居民生活垃圾 热值 BP神经网络 RBF神经网络 ANFIS自适应神经模糊推理系统 |
| 收稿时间： | 2014-12-03 |
Prediction of household waste combustible component calorific value based on artificial neural network |
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| Ding Lan, Zhang Wenyang, Zhang Liangjun, Chen Junde. Prediction of household waste combustible component calorific value based on artificial neural network[J]. Chinese Journal of Environmental Engineering, 2016, 10(2): 899-905. doi: 10.12030/j.cjee.20160261 |
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| Authors: | Ding Lan Zhang Wenyang Zhang Liangjun Chen Junde |
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| Affiliation: | 1.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China; 2.Guangzhou TipDM Software Technology Co. Ltd., Guangzhou 510665, China |
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| Abstract: | This study established combustible-component calorific-value-prediction models based on a back propagation (BP) neural network, radical basis function neural network (RBF), and adaptive neural fuzzy inference system (ANFIS) for resident waste. The results showed that the prediction accuracy of the BP neural network model, the RBF model, and the ANFIS model were 93.36%, 96.87%, and 91.06%, respectively. Each model can be used to predict calorific value, but the RBF model has the highest prediction accuracy, and is thus most suitable for estimating the calorific value of combustible waste. |
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| Keywords: | household waste calorific value BP neural network radical basis function neural network adaptive neural fuzzy inference system |
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