共查询到16条相似文献,搜索用时 187 毫秒
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为了预估混合底物碳源条件下活性污泥PHA合成产量预测的准确度,通过引入遗传算法对BP人工神经网络的权值和阈值进行优选,建立基于GA-BP神经网络的餐厨垃圾合成PHA工艺产量预测模型。以餐厨垃圾发酵液为底物碳源,利用活性污泥在ADD模式下进行PHA合成。以实验数据为基础训练神经网络模型,通过实测数据与模型预测数据之间的对比,验证了人工神经网络预测模型的精确度,并对长期PHA合成能力进行了预测。结论表明:基于遗传算法改进的GA-BP网络模型表现出比传统BP神经网络模型更佳的预测准确度,为评估混合菌群PHA最大合成产量的长期发展趋势,确定合理富集时长探索了可行方法。 相似文献
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神经网络在海水腐蚀预测中的应用 总被引:1,自引:1,他引:1
根据我国材料自然环境腐蚀网站长期以来积累的海水腐蚀数据,采用BP人工神经网络算法,建立了碳钢及低合金钢的海水腐蚀预测模型.该模型以合金成分、环境因素为输入参数,以平均腐蚀速率为输出参数.以碳钢、低合金钢的17种钢种在青岛、厦门、榆林海水腐蚀试验站16年腐蚀数据建模.选定A3钢与10CrCuSiV在以上三地16年的腐蚀数据为验证样本.结果表明该网络具有良好的预测精度,能够正确反映海水环境腐蚀性因素及金属材料腐蚀暴露时间与其腐蚀速率的关系,用于碳钢及低合金钢在海洋全浸环境中的腐蚀预测. 相似文献
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灰色神经网络模型在油气管道腐蚀速度预测中的应用 总被引:8,自引:0,他引:8
简要说明了GM(1,1)模型和BP神经网络模型预测过程,提出了灰色神经网络组合模型,用此方法对某原油长输管道腐蚀速度进行了预测,并用MatLab语言编程的方法对数据做了处理,结果表明用该方法预测得到的数据与实测值非常接近。 相似文献
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提出一种基于遗传算法优化的BP神经网络(GA-BP)的化工园区应急救援能力可靠性分析模型。通过事故树分析对16家化工园区应急救援能力的可靠性进行量化,并作为GA-BP神经网络模型训练的输出值;以事故树中的28项基本事件为依据进行分类总结,建立化工园区应急救援能力层次分析评估指标体系,在日常生产状态下的应急系统维护与事故时的应急处置能力这两个准则层下分为要素层,包括应急系统硬件维护、应急救援人员管理、应急管理机构、应急预案与演练和信息传递、应急人员动员、现场处置、事故后恢复能力,指标层元素分别对应事故树的基本事件,并计算指标层元素相对于目标层的复合权重,再以调查问卷的方式邀请专家对化工园区的指标层元素进行打分,将每一园区的各项要素得分与复合权重相乘作为GA-BP神经网络模型的输入值;从样本组中选取12组作为训练样本、4组作为测试样本,验证建立的GA-BP神经网络模型的可行性,并与传统BP神经网络的分析数据进行对比。结果显示:GA-BP神经网络输出数据的平均误差为3.83%,均方误差为0.002;而BP神经网络输出数据的平均误差为8.13%,均方误差为0.004;GA-BP神经网络的分析结果与事故树的分析结果更为接近,且相对于事故树分析减少了复杂的建树过程,具有更高的易用性。 相似文献
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目前激光荧光遥感是探测海面溢油最有效的工具。作者将神经网络(ANN)的自组织特征映射SOM模型引入激光遥感的荧光光谱鉴别领域。本文主要进行的是理论建模和分析工作,而且用计算机软件方法实现了神经网络的模式识别和分类功能,对推广能力进行了实验分析。经过改进神经网络已具有比较理想的推广能力,并认为SOM应作为溢油识别的较理想方法。 相似文献
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Fatemeh Ghanbary Nasser Modirshahl Morteza Khosravi Mohammad Ali Behnajady 《环境科学学报(英文版)》2012,24(4):750-756
Titanium dioxide (TiO2) nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitrophenol (4-NP). All catalysts were analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM). An artificial neural network model (ANN) was developed to predict the photocatalytic removal of 4-NP in the presence of TiO2 nanoparticles prepared under desired conditions. The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions. 相似文献
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人工神经网络模型在水质预警中的应用研究进展 总被引:1,自引:0,他引:1
水质预警模型是大数据时代构建环境智能决策与管理体系的关键技术.近年来,水质自动化监测能力的提升以及测管协同对环境模型的强烈需求,激发了研究人员探索新的建模方法并努力提高模型预测性能.其中,人工神经网络(Artificial Neural Network, ANN)模型发展迅速.本文综述了3大类ANN模型的发展历史和模型结构特点,梳理了ANN模型在水质数据软测量、数据异常检测和时间序列预测等方面的研究进展,归纳了一般建模流程、技术建议和常用的模型性能指标,发现ANN模型的应用依赖于监测数据质量,存在模型可解释性差、模型运行硬件资源要求较高等不足,提出未来水质预警模型的研发思路和重点,需要加快推进水环境监测技术与预警模型的协同发展和业务化应用,通过多种应用场景检验实现技术迭代,形成大数据驱动的水质在线监测-智能预警-应急管理支撑体系,助力我国环境治理能力现代化. 相似文献
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Eunjeong Lee Chounghyun Seong Hakkwan Kim Seungwoo Park Moonseong Kang 《环境科学学报(英文版)》2010,22(6):840-845
This study described the development and validation of an artificial neural network (ANN) for the purpose of analyzing the e ects of
climate change on nonpoint source (NPS) pollutant loads from agricultural small watershed. The runo discharge was estimated using
ANN algorithm. The performance of ANN model was examined using observed data from study watershed. The simulation results
agreed well with observed values during calibration and validation periods. NPS pollutant loads were calculated from load-discharge
relationship driven by long-term monitoring data. LARS-WG (Long Ashton Research Station-Weather Generator) model was used
to generate rainfall data. The calibrated ANN model and load-discharge relationship with the generated data from LARS-WG were
applied to analyze the e ects of climate change on NPS pollutant loads from the agricultural small watershed. The results showed that
the ANN model provided valuable approach in estimating future runo discharge, and the NPS pollutant loads. 相似文献
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In recent years, with the raising of awareness in environmental protection and sustainable development in enterprises, the green issue has become more and more critical in supply chain management. This study intends to develop a green supplier selection model which integrates artificial neural network (ANN) and two multi-attribute decision analysis (MADA) methods: data envelopment analysis (DEA) and analytic network process (ANP). It is called ANN–MADA hybrid method. ANN–MADA hybrid method considers both practicality in traditional supplier selection criteria and environmental regulations. It also overcomes traditional DEA drawbacks, limitations of data accuracy and decision-making units (DMUs) amounts constraint. The model evaluation results of an international well-known camera manufacturer indicate that the ANN–MADA hybrid method outperforms two other hybrid methods, ANN–DEA and ANP–DEA. It was also discovered that ANN–MADA has better power of discrimination and noise-insensitivity in evaluating green suppliers’ performances. 相似文献