基于智能方法的蒸发型空调系统性能实验与优选研究
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摘要
本文在分析和总结前人在蒸发型空调技术研究成果的基础上首次提
出了新的研究理论和方法 以实验为基础 从研究理论和方法的对比选择
和实验论证入手进行了深入的理论与实验研究得出了几个有意义的结
论和创新点为所提出的新研究理论和方法的实际应用奠定初步基础
主要结论如下
1 采用均匀实验可以明显地减少实验次数和实验误差节约实验费用和
时间
2 对于均匀试验所获得的大量离散型非线性实验数据可以运用主成
分分析法和模糊聚类分析法对其进行整理和排序并取得预期的目的
3 BP神经网络完全可以应用于预测蒸发型空调系统输出性能预测相对
误差在4%之内 完全能够满足一般工程需要
4 应用BP 神经网络综合评价模型可以避免评价过程中的人为失效
且具有很强的容错能力
5 采用模糊综合评估的方法进行填料性能比较与优选可以从中选取令
人满意的方案
主要创新点如下
1 首次在蒸发型空调系统性能实验研究中提出并运用均匀实验方法
2 首次在离散型非线性实验数据处理和分析中提出并运用主成分分析
和模糊聚类分析的新方法
3 首次提出把神经网络运用于蒸发型空调系统性能预测中且效果良好
4 首次提出应用神经网络对蒸发型空调系统性能进行综合评价的模型
5 首次提出并应用模糊综合评估的方法进行蒸发型空调系统方案比较与
填料优选
Based on analysis and summary of predecessors. research results on
evaporative type air-condition(ETAC)technology,a novel research theory and
method(RT&M) is presented in this paper. With the aid of experiment, and
starting with RT&M. contrast selection and experiment demonstration, further
theory and experiment study is processed and some significant conclusions
and innovative points are obtained, whichestablishes foundation for the
application of the novel RT&M.
Main conclusions are as following:
(1).Utilizing uniform experiment design can obviously lessen experiment time
and errors, as well as save experimental fee and time.
(2).Principal component analytical method and fuzzy classification method can
be used to composeand sortthe discretive and nonlinear data obtained from the
uniform experiment,and the results are as anticipated.
(3).BP neural network can be used to predict the output performance of ETAC
system, and all relative errors are within 4%, can entirely meet the common
engineering needs.
(4).The BP neural network comprehensive evaluation(CE) model can avoid
man-made invalidation during the evaluation process,and have strong error
tolerance capability.
(5).Satisfying solution can be attained by using the fuzzyCE method for
comparing and optimum seeking the filler.
Main innovative points are as following:
1 Initially put forward and apply uniform experiment method in the study of
ETAC performance experiment.
2 Initiallyput forward and apply novel methods of principal component
4
第一章 绪论
analysis method and fuzzyclassification method duringdiscretive and
nonlinear experimental data processing and analysis.
3 Initiallyput forward and applyBP neural network in predicting the output
performance of ETAC system, and the results are satisfying.
4 Initiallyput forward and applyBP neural network in establishing the CE
model of the output performance of ETAC system.
5 Initiallyput forward and applythe fuzzy CE method to comparing and
optimum seeking the filler.
出了新的研究理论和方法 以实验为基础 从研究理论和方法的对比选择
和实验论证入手进行了深入的理论与实验研究得出了几个有意义的结
论和创新点为所提出的新研究理论和方法的实际应用奠定初步基础
主要结论如下
1 采用均匀实验可以明显地减少实验次数和实验误差节约实验费用和
时间
2 对于均匀试验所获得的大量离散型非线性实验数据可以运用主成
分分析法和模糊聚类分析法对其进行整理和排序并取得预期的目的
3 BP神经网络完全可以应用于预测蒸发型空调系统输出性能预测相对
误差在4%之内 完全能够满足一般工程需要
4 应用BP 神经网络综合评价模型可以避免评价过程中的人为失效
且具有很强的容错能力
5 采用模糊综合评估的方法进行填料性能比较与优选可以从中选取令
人满意的方案
主要创新点如下
1 首次在蒸发型空调系统性能实验研究中提出并运用均匀实验方法
2 首次在离散型非线性实验数据处理和分析中提出并运用主成分分析
和模糊聚类分析的新方法
3 首次提出把神经网络运用于蒸发型空调系统性能预测中且效果良好
4 首次提出应用神经网络对蒸发型空调系统性能进行综合评价的模型
5 首次提出并应用模糊综合评估的方法进行蒸发型空调系统方案比较与
填料优选
Based on analysis and summary of predecessors. research results on
evaporative type air-condition(ETAC)technology,a novel research theory and
method(RT&M) is presented in this paper. With the aid of experiment, and
starting with RT&M. contrast selection and experiment demonstration, further
theory and experiment study is processed and some significant conclusions
and innovative points are obtained, whichestablishes foundation for the
application of the novel RT&M.
Main conclusions are as following:
(1).Utilizing uniform experiment design can obviously lessen experiment time
and errors, as well as save experimental fee and time.
(2).Principal component analytical method and fuzzy classification method can
be used to composeand sortthe discretive and nonlinear data obtained from the
uniform experiment,and the results are as anticipated.
(3).BP neural network can be used to predict the output performance of ETAC
system, and all relative errors are within 4%, can entirely meet the common
engineering needs.
(4).The BP neural network comprehensive evaluation(CE) model can avoid
man-made invalidation during the evaluation process,and have strong error
tolerance capability.
(5).Satisfying solution can be attained by using the fuzzyCE method for
comparing and optimum seeking the filler.
Main innovative points are as following:
1 Initially put forward and apply uniform experiment method in the study of
ETAC performance experiment.
2 Initiallyput forward and apply novel methods of principal component
4
第一章 绪论
analysis method and fuzzyclassification method duringdiscretive and
nonlinear experimental data processing and analysis.
3 Initiallyput forward and applyBP neural network in predicting the output
performance of ETAC system, and the results are satisfying.
4 Initiallyput forward and applyBP neural network in establishing the CE
model of the output performance of ETAC system.
5 Initiallyput forward and applythe fuzzy CE method to comparing and
optimum seeking the filler.
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