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人工神经网络(ANN)是复杂非线性科学和人工智能科学的前沿,其在水质评价的应用研究在国内外尚处于初创阶段。目前得到普遍应用的是采用BP算法的多层前馈神经网络。文中运用人工神经网络的反向传播(BP)算法对大冶市两个湖泊水质进行了评价,与模糊数学等方法相比,评价精度较高,方法简单易行。 相似文献
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基于遗传算法的人工神经网络在降水酸度预测中的应用 总被引:1,自引:0,他引:1
在误差反向传播(Back Propagation)算法的人工神经网络结构模型的基础上,应用遗传算法训练神经网络权重,实现网络结构的优化。用优化后的BP人工神经网络建立了江西省南昌市的降水酸度预测模型。并将该模型预测结果与BP算法和多元线性回归法的预测结果进行了比较。检验结果表明:基于遗传算法的人工神经网络优于BP算法及多元线性回归法,具有良好的预测效果。 相似文献
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根据西安市雁塔区小寨环境空气监测点2011年7月31日起400 d的SO224小时平均浓度监测数据时间序列建立BP人工神经网络(ANN)预测模型,并用接下来100 d的数据对模型的仿真性能进行检验,从而验证了BP人工神经网络模型预测环境空气SO224小时平均浓度的可行性与准确度。经反复调试,最终选用2-3-1的网络结构并以trainbr作为训练算法,经34次迭代网络收敛,耗时7 s,预测结果相对于实际监测数据的平均绝对百分比误差为0.082,模型显示出良好的预测性能。预测结果表明,结构设定合理、训练算法选用适宜的BP人工神经网络模型能较好地反映SO2浓度的动态变化规律,具有可行性。 相似文献
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大气环境质量的人工神经网络决策模型 总被引:23,自引:0,他引:23
为了对环境质量进行综合评价,运用误差反向传播算法的人工神经网络方法建立了环境质量评价的B-P决策模型,用此模型研究实例的大气环境质量,结果表明B-P网络用于环境质量评价具有客观性和实用性 相似文献
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B-P网络用于水质综合评价方法的研究 总被引:13,自引:2,他引:13
本文应用误差反向传播(B—P)算法的人工神经网络建立了水质综合评价模型。该模型应用于实例的水质评价结果表明B—P网络用于水质综合评价具有客观性和实用性。 相似文献
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B-P网络用于水质综合评价方法的研究 总被引:15,自引:1,他引:15
本文应用误差反向传播(B—P)算法的人工神经网络建立了水质综合评价模型。该模型应用于实例的水质评价结果表明B—P网络用于水质综合评价具有客观性和实用性。 相似文献
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ABR反应器的处理效率受多方面的因素影响,本文通过BP人工神经网络,利用ABR反应器进水CODcr浓度、容积负荷、温度、稳定运行时间四个参数对其反应器处理效率进行预测。结果表明,BP人工神经网络可较好的用于ABR反应器处理效率的预测,具有较高的精度,在实际生产中,可以运用人工神经网络,对ABR反应器的运行参数进行调整,使之达到最优化的运行状态。 相似文献
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Peroxyacyl nitrates (PANs) are important secondary pollutants in ground-level atmosphere. Accurate prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation (BP) artificial neural network (ANN) and multiple linear regression (MLR) methods were used to predict the hourly average PAN concentrations at Peking University, Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient (R) of 0.7089, mean bias error of ? 0.0043 ppb, mean absolute error of 0.4836?ppb, root mean squared error of 0.5320?ppb, and Willmott's index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters, providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control. 相似文献
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为比较响应面法与反向传播神经网络法在厌氧发酵过程中的应用效果,以青霉素菌渣为原料,通过单因素和Box-Behnken法设计试验,在发酵体系中添加不同量的Fe2+、Co2+、Ni2+,以确定其对青霉素菌渣厌氧产气性能的影响.结果表明,Fe2+、Co2+、Ni2+单一最佳添加量为:500mg/L、30mg/L、0.3mg/L,产沼气量较对照分别提高了:102.18%、45.48%、60.12%.其促进作用随添加浓度增大呈现:弱-强-弱趋势.使用响应面法及反向传播神经网络法对金属离子添加量进行建模优化,并使用批式厌氧发酵进行验证.响应面法建模预测Fe2+、Co2+、Ni2+最佳混合添加浓度为:440.94mg/L、16.22mg/L、0.39mg/L,预测累积产沼气量为1314.49mL,R2=0.972,试验与验证相对误差为4.65%;反向传播神经网络法建模Fe2+、Co2+、Ni2+最佳混合添加浓度为495mg/L、21mg/L、0.5mg/L,预测产沼气量为1551.55mL,R2=0.991,试验与验证相对误差为0.47%.反向传播神经网络法建模具有更好的拟合效果且与验证试验误差小,是一种更有效的仿真方法.说明该方法在优化厌氧发酵金属离子添加具有应用潜力,同时也为厌氧发酵条件优化提供新思路. 相似文献
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An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guanting reservoir inlet and outlet(China). The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What's more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting. 相似文献
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An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guanting reservoir inlet and outlet (China). The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What's more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting. 相似文献
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An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guanting reservoir inlet and outlet (China). The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What's more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting. 相似文献
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