In this paper, a new ergodic property analysis model of hydrological process is proposed based on fuzzy-rough c-means clustering (FRCM), autocorrelogram, and fuzzy least absolute regression (FLAR). A precipitation tim...In this paper, a new ergodic property analysis model of hydrological process is proposed based on fuzzy-rough c-means clustering (FRCM), autocorrelogram, and fuzzy least absolute regression (FLAR). A precipitation time series (1951―2004) from Shanghai Hydrology Station is then analyzed with the model. The results show that the precipitation time series of April, May, June, and September has er-godic property. We conclude that in the long run, the precipitation of April, May, June, and September will not keep decreasing; it will converge to its mean value in some period.展开更多
Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a...Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.展开更多
烧结过程的运行性能是生产效率和能源利用的综合表现.运行性能评价是保持烧结过程的运行性能处于最优等级的前提.考虑到时间序列数据的冗余,提出一种基于粒度聚类的铁矿石烧结过程运行性能评价方法.首先,利用单因素方差分析方法选取影...烧结过程的运行性能是生产效率和能源利用的综合表现.运行性能评价是保持烧结过程的运行性能处于最优等级的前提.考虑到时间序列数据的冗余,提出一种基于粒度聚类的铁矿石烧结过程运行性能评价方法.首先,利用单因素方差分析方法选取影响运行性能等级的检测参数;然后,采用多粒度区间信息粒化实现检测参数时间序列数据的降维,并进行粒度聚类,得到聚类标签;最后,以聚类得到的聚类标签为输入,利用随机森林算法进行运行性能等级评价.利用实际钢铁企业的运行数据进行实验,构建两个对比实验,分别采用基于时间序列数据聚类(Time series data clustering,TSDC)方法和基于时间序列特征聚类(Time series feature clustering,TSFC)方法.实验结果表明,该方法为有效评价烧结过程的运行性能提供了一套可行方案,为操作人员提升烧结过程运行性能提供了有力的指导.展开更多
基金Supported by the National Science and Technology Supporting Project (Grant No.2006BAB04A08)
文摘In this paper, a new ergodic property analysis model of hydrological process is proposed based on fuzzy-rough c-means clustering (FRCM), autocorrelogram, and fuzzy least absolute regression (FLAR). A precipitation time series (1951―2004) from Shanghai Hydrology Station is then analyzed with the model. The results show that the precipitation time series of April, May, June, and September has er-godic property. We conclude that in the long run, the precipitation of April, May, June, and September will not keep decreasing; it will converge to its mean value in some period.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(61225016)the State Key Program of National Natural Science of China(61533002)
文摘Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.
文摘烧结过程的运行性能是生产效率和能源利用的综合表现.运行性能评价是保持烧结过程的运行性能处于最优等级的前提.考虑到时间序列数据的冗余,提出一种基于粒度聚类的铁矿石烧结过程运行性能评价方法.首先,利用单因素方差分析方法选取影响运行性能等级的检测参数;然后,采用多粒度区间信息粒化实现检测参数时间序列数据的降维,并进行粒度聚类,得到聚类标签;最后,以聚类得到的聚类标签为输入,利用随机森林算法进行运行性能等级评价.利用实际钢铁企业的运行数据进行实验,构建两个对比实验,分别采用基于时间序列数据聚类(Time series data clustering,TSDC)方法和基于时间序列特征聚类(Time series feature clustering,TSFC)方法.实验结果表明,该方法为有效评价烧结过程的运行性能提供了一套可行方案,为操作人员提升烧结过程运行性能提供了有力的指导.
