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提出了一种在线测量冷凝器污脏程度的新方法.该方法选取传热端差作为研究对象,以神经网络建模技术为基础成功实现冷凝器污脏、工况参数变化对传热端差影响的分离,可较准确地实现冷凝器污脏的在线监测.在神经网络建模中,采用RBF神经网络描述变工况传热端差变化的非线性过程,研究了一种自适应训练算法动态调整网络结构与参数,从而获得了结构紧凑、精度较高的测量模型,便于实时应用.根据此方法,研制了以DSP为核心的测量仪,并在不同工况和堵管情况下进行了现场试验, 试验结果证明了该方法的有效性. 相似文献
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提出了一种在线监测凝汽器污脏程度的新方法。该方法将传热端差作为研究对象,综合考虑各因素对端差的影响,运用神经网络建模技术成功地实现了凝汽器污脏、工况参数变化对端差影响的分离,可准确地在线监测凝汽器污脏程度。介绍了根据此方法研制的以DSP为核心的监测仪,并进行了现场试验,试验结果证明了该仪器的有效性。 相似文献
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基于T-S模型的冷凝器污垢预测 总被引:2,自引:1,他引:1
针对冷凝器的周期性结垢过程以及工况参数的动态变化,提出了一种冷凝器污垢预测的新方法.该方法将污垢分解为软垢和硬垢两部分,并采用两个T-S模糊模型分别描述软垢和硬垢的变化趋势,进而通过二者的结合获得较为精确的污垢预测.根据此方法,进行了现场试验, 试验结果表明:与渐近污垢模型及改进的渐近污垢模型相比,该方法能够有效地处理冷凝器的周期性结垢现象,并在冷凝器工况参数变化时仍然取得较满意的预测精度.该方法的成功应用为冷凝器最优清洗机制的建立奠定了基础. 相似文献
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针对目前静态软测量建模方法无法反映工业过程动态信息,造成模型预测精度低、鲁棒性差等问题,提出了一种基于模糊曲线和高斯过程的动态软测量建模方法.该方法采用模糊曲线法对输入数据进行处理,并利用处理后的数据构建新的数据集,最后采用高斯过程建立软测量模型.将提出的动态软测量模型应用于PTA氧化过程中4-CBA含量的预测,结果表明,所建模型运算速度快、预测精度高. 相似文献
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基于多模型组合的冷凝器中污垢预测 总被引:2,自引:0,他引:2
提出了一种基于多模型组合的冷凝器污垢预测新方法.该方法采用经验模型、自适应指数平滑模型、灰色模型、T―S模糊模型等多种模型预测污垢的增长,并通过遗传算法对模型参数、各模型输出之间的组合系数进行自适应滚动优化调整,以适应冷凝器水质及工况参数的动态变化,从而取得比单个预测模型更好的预测精度.试验结果表明:该方法短期污垢预测效果好,中长期污垢预测精度较高,是实现冷凝器污垢预测的有力工具. 相似文献
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针对冷凝器的周期性结垢过程以及工况参数的动态变化,提出了一种冷凝器污垢预测的新方法。该方法将
污垢分解为软垢和硬垢两部分,并采用两个T2S 模糊模型分别描述软垢和硬垢的变化趋势,进而通过二者的结合获
得较为精确的污垢预测。根据此方法,进行了现场试验, 试验结果表明:与渐近污垢模型及改进的渐近污垢模型相
比,该方法能够有效地处理冷凝器的周期性结垢现象,并在冷凝器工况参数变化时仍然取得较满意的预测精度。
该方法的成功应用为冷凝器最优清洗机制的建立奠定了基础。 相似文献
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基于粗糙集理论的模糊模型及其在复杂控制系统中的应用 总被引:2,自引:0,他引:2
将粗糙集理论与模糊逻辑技术相结合,提出一种建立粗糙模糊模型的建模方法.运用粗糙集理论的基本概念和简约计算方法,从大量原始数据中发现精简的、概略化的规则来建立粗糙模糊模型,并提出了对模型进行扩充与完备化的概念.将该方法应用于化纤工业中抽丝冷却侧吹风非线性控制过程的仿真实验研究表明了该建模方法的有效性和可行性. 相似文献
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模糊灰色认知网络的建模方法及应用 总被引:1,自引:0,他引:1
针对具有不确定性非线性系统的机理模型难以建立的问题,提出了基于模糊灰色认知网络(Fuzzy grey cognitive networks,FGCN)的非线性系统建模方法.该方法将模糊认知网络和灰色系统理论相结合,把模糊认知网络的节点状态值和权值扩展为灰色区间,引入灰度来评判可靠性.采用一种带终端约束的非线性Hebbian学习算法(Nonlinear hebbian learning,NHL)辨识FGCN的模型参数,引入了与FGCN模型中节点的系统实际测量值对应的灰数值,在更新机制中增加了包含系统测量值与预测值之差的修正项,对权值进行有监督的修正.利用水箱控制系统进行的仿真实验结果表明,本文提出的建模方法能解决对数据存在不确定性或缺失的复杂系统建模的难题,所建的模型能做出接近人类智能的控制决策,所采用的权值学习方法具有收敛速度快、学习结果精准等优点,并克服了传统非线性Hebbian算法对初始值依赖性强的缺点,对不确定性系统的建模具有广泛适用性. 相似文献
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Aiming at the deficiencies of analysis capacity from different levels and fuzzy treating method in product function modeling of conceptual design,the theory of quotient space and universal triple I fuzzy reasoning method are introduced,and then the function modeling algorithm based on the universal triple I fuzzy reasoning method is proposed.Firstly,the product function granular model based on the quotient space theory is built,with its function granular representation and computing rules defined at the same time.Secondly,in order to quickly achieve function granular model from function requirement,the function modeling method based on universal triple I fuzzy reasoning is put forward.Within the fuzzy reasoning of universal triple I method,the small-distance-activating method is proposed as the kernel of fuzzy reasoning;how to change function requirements to fuzzy ones,fuzzy computing methods,and strategy of fuzzy reasoning are respectively investigated as well;the function modeling algorithm based on the universal triple I fuzzy reasoning method is achieved.Lastly,the validity of the function granular model and function modeling algorithm is validated.Through our method,the reasonable function granular model can be quickly achieved from function requirements,and the fuzzy character of conceptual design can be well handled,which greatly improves conceptual design. 相似文献
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Based on data driven modeling theory, PVC polymerization process modeling and intelligent optimization control algorithm is studied. Firstly, a multi-T–S fuzzy neural networks soft-sensing model combining the principal component analysis (PCA) and fuzzy c-means (FCM) clustering algorithm is proposed to predict the convention rate and velocity of Vinyle Chloride Monomer (VCM). The proposed hybrid learning algorithm utilizing the harmony search (HS) and least square method is used to adjust the model premise parameters and consequent parameters. Secondly, the generalized predictive control (GPC) algorithm of polymerizer temperature based on segmental affine is proposed. According to dynamic equation of polymerizer temperature deduced by heat balance mechanism, the segmental affine model is built by temperature and convention rate of the polymerizer. Then linear matrix inequality (LMI) method is used to design the controller. Finally, simulation results and industrial application show the validity and feasibility of the proposed control strategy. 相似文献
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Chen-Chia Chuang Shun-Feng Su Song-Shyong Chen 《Fuzzy Systems, IEEE Transactions on》2001,9(6):810-821
The Takagi-Sugeno-Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Most approaches for modeling TSK fuzzy rules define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, training data sets algorithms often contain outliers, which seriously affect least-square error minimization clustering and learning algorithms. A robust TSK fuzzy modeling approach is presented. In the approach, a clustering algorithm termed as robust fuzzy regression agglomeration (RFRA) is proposed to define fuzzy subspaces in a fuzzy regression manner with robust capability against outliers. To obtain a more precision model, a robust fine-tuning algorithm is then employed. Various examples are used to verify the effectiveness of the proposed approach. From the simulation results, the proposed robust TSK fuzzy modeling indeed showed superior performance over other approaches 相似文献