SMOOTH SURFACE INTERPOLATION OVER ARBITRARY TRIANGULATIONS BY SUBDIVISION ALGORITHMS

ＳＭＯＯＴＨＳＵＲＦＡＣＥＩＮＴＥＲＰＯＬＡＴＩＯＮＯＶＥＲＡＲＢＩＴＲＡＲＹＴＲＩＡＮＧＵＬＡＴＩＯＮＳＢＹＳＵＢＤＩＶＩＳＩＯＮＡＬＧＯＲＩＴＨＭＳＲｕｉｂｉｎＱｕＳＭＯＯＴＨＳＵＲＦＡＣＥＩＮＴＥＲＰＯＬＡＴＩＯＮＯＶＥＲＡＲＢＩＴＲＡＲＹＴＲ...     

ARTISTIC CHARM OF MANDELBROT FAMILY AND JULIA FAMILY——A FURTHER DISCUSSION CONCERNING MANDELBROT FAMILY AND JULIA FAMILY

ＡＲＴＩＳＴＩＣＣＨＡＲＭＯＦＭＡＮＤＥＬＢＲＯＴＦＡＭＩＬＹＡＮＤＪＵＬＩＡＦＡＭＩＬＹ———ＡＦＵＲＴＨＥＲＤＩＳＣＵＳＳＩＯＮＣＯＮＣＥＲＮＩＮＧＭＡＮＤＥＬＢＲＯＴＦＡＭＩＬＹＡＮＤＪＵＬＩＡＦＡＭＩＬＹＷａｎｇＹａｎＴａｏＭａｏｑｉＸｕＭａ...     

CURVE AND SURFACE INTERPOLATIONBY SUBDIVISION ALGORITHMS

ＣＵＲＶＥＡＮＤＳＵＲＦＡＣＥＩＮＴＥＲＰＯＬＡＴＩＯＮＢＹＳＵＢＤＩＶＩＳＩＯＮＡＬＧＯＲＩＴＨＭＳＲｕｉｂｉｎＱｕＣＵＲＶＥＡＮＤＳＵＲＦＡＣＥＩＮＴＥＲＰＯＬＡＴＩＯＮＢＹＳＵＢＤＩＶＩＳＩＯＮＡＬＧＯＲＩＴＨＭＳ￥ＲｕｉｂｉｎＱｕＡｂｓｔｒａ...     

基于方块脉冲函数逼近的线性连续回归模型的参数估计及其应用

基于方块脉冲函数逼近的线性连续回归模型的参数估计及其应用赵明旺（武汉钢铁学院）ＰＡＲＡＭＥＴＥＲＥＳＴＩＭＡＴＩＯＮＦＯＲＬＩＮＥＡＲＣＯＮＴＩＮＵＯＵＳＲＥＧＲＥＳＳＩＶＥＳＹＳＴＥＭＳＶＩＡＢＬＯＣＫＰＵＬＳＥＦＵＮＣＴＩＯＮＳＡＮＤＩＴＳＡＰＰ...     

常微分方程初值问题的并行算法

常微分方程初值问题的并行算法刘德贵，宋晓秋（北京计算机应用和仿真技术研究所）ＰＡＲＡＬＬＥＬＡＬＧＯＲＩＴＨＭＳＦＯＲＩＮＩＴＩＡＬＶＡＬＵＥＰＲＯＢＬＥＭＳＦＯＲＯＲＤＩＮＡＲＹＤＩＦＦＥＲＥＮＴＩＡＬＥＱＵＡＴＩＯＮＳ￥ＬｉｕＤｅｇｕｉ；Ｓｏｎｇ...     

FRAMEWORK OF A DISTRIBUTED ENGINEERING DBMS

ＦＲＡＭＥＷＯＲＫＯＦＡＤＩＳＴＲＩＢＵＴＥＤＥＮＧＩＮＥＥＲＩＮＧＤＢＭＳＷａｎｇＴａｏ；ＬｉｎＺｏｎｇｋａｉ；ＧｕｏＹｕｃｈａｉＳＭＯＯＴＨＳＵＲＦＡＣＥＩＮＴＥＲＰＯＬＡＴＩＯＮＯＶＥＲＡＲＢＩＴＲＡＲＹＴＲＩＡＮＧＵＬＡＴＩＯＮＳＢＹＳＵＢＤ...     

解线代数方程组的游动点估计量零方差Monte Carlo迭代格式

解线代数方程组的游动点估计量零方差ＭｏｎｔｅＣａｒｌｏ迭代格式冯庭桂（北京应用物理与计算数学研究所）ＡＺＥＲＯＶＡＲＩＡＮＣＥＩＴＥＲＡＴＩＯＮＳＣＨＥＭＥＯＦＣＯＬＬＩＳＩＯＮＥＳＴＩＭＡＴＯＲＦＯＲＳＯＬＶＩＮＧＬＩＮＥＡＲＡＬＧＥＢＲＡＩＣＥＱ...     

线性系统的一种数字仿真方法

线性系统的一种数字仿真方法费景高（北京计算机应用和仿真技术研究所）ＡＭＥＴＨＯＤＦＯＲＤＩＧＩＴＡＬＳＩＭＵＬＡＴＩＯＮＯＦＬＩＮＥＡＲＳＹＳＴＥＭＳ￥ＦｅｉＪｉｎｇ－ｇａｏ（ＢｅｉｊｉｎｇＩｎｓｔｉｔｕｔｅｏｆＣｏｍｐｕｔｅｒＡｐｐｌｉｃａｔｉｏｎ...     

INSTRUCTIONS TO AUTHORS

ＩＮＳＴＲＵＣＴｌＯＮＳＴＯＡＵＴＨＯＲＳＳＭＯＯＴＨＳＵＲＦＡＣＥＩＮＴＥＲＰＯＬＡＴＩＯＮＯＶＥＲＡＲＢＩＴＲＡＲＹＴＲＩＡＮＧＵＬＡＴＩＯＮＳＢＹＳＵＢＤＩＶＩＳＩＯＮＡＬＧＯＲＩＴＨＭＳ￥ＲｕｉｂｉｎＱｕＳｕｂｍｉｓｓｉｏｎｏｆＭａｎｕｓｃｒ...     

一类流体力学程序的向量化与并行化

一类流体力学程序的向量化与并行化袁国兴，张宝琳（北京应用物理与计算数学研究所）ＰＡＲＡＬＬＥＬＩＺＡＴＩＯＮＦＯＲＳＯＭＥＴＷＯ－ＤＩＭＥＮＳＩＯＮＡＬＦＬＵＩＤＤＹＮＡＭＩＣＡＬＰＲＯＧＲＡＭＳ￥ＹｕａｎＧｕｏｘｉｎｇ；ＺｈａｎｇＢａｏｌｉｎ（Ｉｎ...     

Integration of support vector regression and annealing dynamical learning algorithm for MIMO system identification

This paper presents a robust approach to identify multi-input multi-output (MIMO) systems. Integrating support vector regression (SVR) and annealing dynamical learning algorithm (ADLA), the proposed method is adopted to optimize a radial basis function network (RBFN) for identification of MIMO systems. In the system identification, first, SVR is adopted to determine the number of hidden layer nodes, the initial structure of the RBFN. After initialization, ADLA with nonlinear time-varying learning rate is then applied to train the RBFN. In the ADLA, the determination of the learning rate would be an important work for the trade-off between stability and speed of convergence. A computationally efficient optimization method, particle swarm optimization (PSO) method, is adopted to simultaneously find optimal learning rates. Due to the advantages of SVR and ADLA (SVR-ADLA), the proposed RBFN (SVR-ADLA-RBFN) has good performance for MIMO system identification. Two examples are illustrated to show the feasibility and superiority of the proposed SVR-ADLA-RBFNs for identification of MIMO systems. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.     

Artificial neural networks for water quality soft-sensing in wastewater treatment: a review

This paper aims to present a comprehensive survey on water quality soft-sensing of a wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We mainly present problem formulation of water quality soft-sensing, common soft-sensing models, practical soft-sensing examples and discussion on the performance of soft-sensing models. In details, problem formulation includes characteristic analysis and modeling principle of water quality soft-sensing. The common soft-sensing models mainly include a back-propagation neural network, radial basis function neural network, fuzzy neural network (FNN), echo state network (ESN), growing deep belief network and deep belief network with event-triggered learning (DBN-EL). They are compared in terms of accuracy, efficiency and computational complexity with partial-least-square-regression DBN (PLSR-DBN), growing ESN, sparse deep belief FNN, self-organizing DBN, wavelet-ANN and self-organizing cascade neural network (SCNN). In addition, this paper generally discusses and explains what factors affect the accuracy of the ANNs-based soft-sensing models. Finally, this paper points out several challenges in soft-sensing models of WWTP, which may be helpful for researchers and practitioner to explore the future solutions for their particular applications.

     

Enhanced Neural Network Modelling for a Real Multi-variable Chemical Process

Application of several Neural Network (NN) modelling techniques to model a Multi-Input Multi-Output (MIMO) nonlinear chemical process is investigated. The process is a laboratory scale chemical reactor with three inputs and three outputs. It typically represents industrial processes due to its nonlinearity, coupling effects and lack of a mathematical model. Different techniques have been used in collecting training data from the reactor. A novel method was used to select the model order and time-delay to determine the NN model input. A Radial Basis Function Network (RBFN) model was then developed. A Recursive Orthogonal Least Squares (ROLS) algorithm was applied as a numerically robust method to update the RBFN weight matrix. In this way, degradation of the modelling error due to ill-conditioning in the training data is avoided. Real data experiments show that the RBFN model developed has high accuracy.     

软测量技术在DeltaV DCS上的实现和应用

针对丁二烯生产装置精馏塔塔顶控制回路存在的问题,建立了软测量模型,并设计了串级推断控制回路。利用从集散控制系统采集的大量现场数据,运用基于多元线性回归方法的软测量建模技术,建立了塔顶中丁二烯产品纯度的软测量模型,实现了产品质量闭环控制。通过DeltaV DCS系统实现控制回路的改造。实际使用证明,该软测量模型具有良好的特性,较高的估计精度和实时性;塔的控制更加平稳,节约了能耗,具有较好的经济效益。     

基于RNN的化工过程软测量模型研究

研究了基于回归神经网络(RNN)为化工颜料锌钡白建立质量指标软测量模型的问题。利用SPSS统计软件对过程历史数据进行预分析处理，进而利用这些数据训练回归神经网络，建立质量指标消色力的软测量模型。针对回归神经网络训练效率低，泛化能力差等问题，尝试引入一种初始权值优化方法加以改进。仿真结果表明，利用回归神经网络可以为此类化工过程建立具有一定预测能力的软测量模型，引入的初始权值优化方法有助于提高回归神经网络初始训效率，但模型的泛化能力还有待进一步改进。     

粗糙分类器的多模型软测量建模方法

根据多模型可以改善模型估计精度,提高泛化性的思想,提出了1种粗糙分类器的多模型软测量建模方法。该方法采用聚类、分类相结合的方式对数据进行分组训练,在一定程度上消除了矛盾样本点可能对模型精度造成的影响。对各组样本利用支持向量回归机建立回归子模型,得到多模型软测量系统。同时,通过向粗糙集引入相似度作为评价样本间相似性的指标,解决了传统粗糙集无法识别训练样本集中未出现过的模式的问题。通过引入概率测度,利用概率公式作为粗糙集分类的决策规则,简化了算法。基于上述理论构造的粗糙分类器,有效地提高了分类器的分类精度,确保了各子模型的估计精度。将该方法应用于双酚A生产过程的质量指标软测量建模,仿真结果表明了该算法的有效性。     

多输入多输出非线性系统的最小二乘支持向量机广义逆控制

针对神经网络逆控制存在的不足, 对一类模型未知且某些状态量较难测得的多输入多输出(MIMO)非线性系统, 在状态软测量函数存在的前提下, 提出一种最小二乘支持向量机(LSSVM)广义逆辨识控制策略. 通过广义逆将原被控系统转化为伪线性复合系统, 并可使其极点任意配置, 采用LSSVM代替神经网络拟合广义逆系统中的静态非线性映射. 将系统的状态量辨识与LSSVM逆模型辨识结合, 通过LSSVM训练拟合同时实现软测量功能. 最后以双电机变频调速系统为对象, 采用该控制策略进行仿真研究, 结果验证了本文算法的有效性.     

Weather analysis using ensemble of connectionist learning paradigms

This paper presents a comparative analysis of different connectionist and statistical models for forecasting the weather of Vancouver, Canada. For developing the models, one year's data comprising of daily temperature and wind speed were used. A multi-layered perceptron network (MLPN) and an Elman recurrent neural network (ERNN) were trained using the one-step-secant and Levenberg–Marquardt algorithm. Radial basis function network (RBFN) was employed as an alternative to examine its applicability for weather forecasting. To ensure the effectiveness of neurocomputing techniques, the connectionist models were trained and tested using different datasets. Moreover, ensembles of the neural networks were generated by combining the MLPN, ERNN and RBFN using arithmetic mean and weighted average methods. Subsequently, performance of the connectionist models and their ensembles were compared with a well-established statistical technique. Experimental results obtained have shown RBFN produced the most accurate forecast model compared to ERNN and MLPN. Overall, the proposed ensemble approach produced the most accurate forecast, while the statistical model was relatively less accurate for the weather forecasting problem considered.     

Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm

Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.

     

基于振动频谱的磨机负荷在线软测量建模

针对磨机负荷(ML)软测量模型难以适应磨矿过程的时变特性,模型需要依据工况实时在线更新的问题,基于磨机简体振动频谱,通过递归主元分析(RPCA)和在线最小二乘支持向量回归机(LSSVR)的集成,提出了ML参数(料球比、矿浆浓度、充填率)在线软测量方法.首先,针对训练样本,采用主元分析(PCA)分别提取振动频谱在低、中、高频段的谱主元;然后以串行组合后的谱主元为输入,采用LSSVR方法构造ML参数离线软测量模型;最后,采用旧模型完成预测后,应用RPCA及在线LSSVR算法分别递归更新模型的输入和模型的回归参数,从而实现了ML软测量模型的在线更新.实验结果表明,该软测量方法与其它常规方法相比具有较高的精度和更好的预测性能.