A heuristic approach to commodity purchasing

In this paper a Heuristic model is proposed for commodity purchasing. The model is based on the combined use of Dynamic Programming model and a price trend forecasting model. The proposed approach is highly efficient and is simple to use even by a layman. Performance of the proposed model is compared with that of the Dynamic Programming model. The results of the sensitivity analysis indicate that the proposed approach performs consistently better compared to that of the Dynamic Programming model. It is. therefore, believed that the proposed approach would be fruitfully utilized in designing an optimal commodity purchasing system.     

基于改进Elmam神经网络的网络流量预测模型

提出了一种改进Elman动态回归神经网络,在此基础上建立了一种网络流量预测模型,该模型相对于传统的线性模型和BP神经网络模型具有更高的预测精度和更好的自适应性,利用某大学校园网统计得到的实际网络流量数据进行仿真实验,结果表明该模型具有良好的预测效果。     

An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem

In this paper, a novel multi objective model is proposed for portfolio selection. The proposed model incorporates the DEA cross-efficiency into Markowitz mean–variance model and considers return, risk and efficiency of the portfolio. Also, in order to take uncertainty in proposed model, the asset returns are considered as trapezoidal fuzzy numbers. Due to the computational complication of the proposed model, the second version of non-dominated sorting genetic algorithm (NSGA-II) is applied. To illustrate the performance of our model, the model is implemented for 52 firms listed in stock exchange market of Iran and the results are analyzed. The results show that the proposed model is suitable in compared with Markowitz and DEA models due to considering return, risk and efficiency, simultaneously.     

Modeling dynamic developable meshes by the Hamilton principle

In this paper, a new dynamic developable surface model is proposed. The proposed model represents developable surfaces using triangle meshes. A novel algorithm is proposed to introduce the Hamilton principle into these meshes such that the resulting developable model is dynamic, i.e., it can offer a time-dependent continuous path to deform the model. Applications with examples are presented; these show that the proposed technique can model buckled developable surfaces well, and can offer physically-realistic animations of deformed developable surfaces.     

Detection of model-plant mismatch in closed-loop control system

The performance of model-based control systems depends a lot on the process model quality, hence the process model-plant mismatch is an important factor degrading the control performance. In this paper, a new methodology based on a process model evaluation index is proposed for detecting process model mismatch in closed-loop control systems. The proposed index is the ratio between the variance of the disturbance innovation and that of the model quality variable. The disturbance innovations are estimated from the routine operation data by an orthogonal projection method. The model quality variable can be obtained using the closed-loop data and the disturbance model estimated by adaptive Least absolute shrinkage and selection operator (Lasso) method. When the order of the disturbance model is less than 2 or the process time delay is large enough, no external perturbations are required. Besides, the proposed index is independent of the controller tuning and insensitive to the changes in disturbance model, which indicates that the proposed method can isolate the process model-plant mismatch from other factors affecting the overall control performance. Three systems with proportional integral (PI) controller, linear quadratic (LQ) controller and unconstrained model predictive control (MPC) respectively are presented as examples to verify the effectiveness of the proposed technique. Besides, Tennessee Eastman process shows the proposed method is able to detect process model mismatch of nonlinear systems.     

考虑硅衬底效应的基于TSV的3D IC电源分配网络建模

基于硅通孔TSV的3D-IC在电源分配网络PDN中引入了新的结构--TSV，另外，3D堆叠使得硅衬底效应成为不可忽略的因素，因此为3D-IC建立PDN模型必须要考虑TSV以及硅衬底效应。为基于TSV的3D-IC建立了一个考虑硅衬底效应的3D PDN模型，该模型由P/G TSV对模型和片上PDN模型组成。P/G TSV对模型是在已有模型基础上，引入bump和接触孔的RLGC集总模型而建立的，该模型可以更好地体现P/G TSV对的电学特性；片上PDN模型则是基于Pak J S提出的模型，通过共形映射法将硅衬底效应引入单元模块模型而建立的，该模型可以有效地反映硅衬底对PDN电学特性的影响。经实验表明，建立的3D PDN模型可以有效、快速地估算3D-IC PDN阻抗。     

基于几何随机的无人机3D信道模型

针对无人机(unmanned aerial vehicle,UAV)多输入多输出(multiple input multiple output,MIMO)信道,提出了使用二维单环和三维半球体建模的无人机信道模型.在提出信道模型的基础上,推导并分析了参考模型和仿真模型的空—时相关函数(space time correlation function,STCF)、多普勒功率谱密度(Doppler power spectral density,DPSD)和二阶统计特性如包络电平交叉率(level crossing rate,LCR).研究了不同的参数对UAV信道统计特性的影响,并将所提模型分别与单球模型和测量值对比.结果表明,莱斯因子、移动散射体速度等对信道特性有显著影响,所提模型时间相关性比单球模型高约20％,并与测量值拟合良好,验证了模型的准确性.     

基于Wiener结构的软测量模型及辨识算法

Wiener模型结构能有效地表征系统的动态和静态特性, 因此这里首先基于这一结构建立软测量模型, 利用动态与静态子模型分别建立辅助变量与主导变量间的动态与静态关系, 并说明该软测量模型的可行性, 给出模型具体表达式. 其次, 针对所提模型, 提出分步辨识方式获得最优模型参数, 说明其可行性. 再次, 为了减少计算和实现模型在线更新, 这里提出参数辨识递推算法, 并给出软测量模型参数的收敛性结论. 通过实例仿真, 可以看出本文提出模型的可行性, 以及分步辨识方式与递推算法的有效性.     

Superior step-size theorem and its application-Parallel variable step-size LMS filters algorithm

With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construct a parallel variable step-size LMS filters algorithm. The theoretical model of the proposed algorithm is analyzed in detail. Simulations show the proposed theoretical model is quite close to the optimal variable step-size LMS (OVS-LMS) model. The experimental learning curves of the proposed algorithm also show the fastest convergence and fine tracking performance.The proposed algorithm is therefore a good realization of the OVS-LMS model.     

Variational PDE based image restoration using neural network

Two variational partial differential equations as regularisation terms are proposed for the image restoration model based on the modified Hopfield neural network. One is based on a harmonic model and the other is based on a total variation model. The performance of these regularisation terms is analysed from the viewpoint of nonlinear diffusion. It can be shown that the two proposed restoration models have edge-preserving performance superior to that of the traditional restoration model. Two algorithms have been proposed on the basis of the harmonic restoration model and the total variation model. Experimental results show that the proposed algorithms are more effective than the traditional algorithm     

Application of fuzzy goal programming approach to multi-objective linear fractional inventory model

In this paper, we propose a model and solution approach for a multi-item inventory problem without shortages. The proposed model is formulated as a fractional multi-objective optimisation problem along with three constraints: budget constraint, space constraint and budgetary constraint on ordering cost of each item. The proposed inventory model becomes a multiple criteria decision-making (MCDM) problem in fuzzy environment. This model is solved by multi-objective fuzzy goal programming (MOFGP) approach. A numerical example is given to illustrate the proposed model.     

基于SSA-AdaBoost-SVM的变压器故障诊断

针对支持向量机(SVM)诊断变压器故障的效果不稳定的问题,利用Ada Boost集成算法对其强化,得到的AdaBoost-SVM模型诊断结果比较稳定,但准确度依然有待提高。因此,提出利用麻雀搜索算法(SSA)对Ada Boost-SVM模型进行优化,指定其弱分类器权重αt、SVM惩罚因子c和核参数g的寻优范围,使用SSA对三种参数在指定的寻优范围内寻优,提高模型的准确率。将提出的SSA-AdaBoost-SVM变压器故障诊断模型与PSO-SVM、SSA-SVM、AdaBoost-SVM、AdaBoost-SSA-SVM和PSO-AdaBoost-SVM五种模型对比,提出的模型具有更高的准确率和稳定性,平均准确率可达91.58%。实验结果表明,提出的SSA-AdaBoost-SVM变压器故障诊断模型具有更好的表现。     

空调蒸发器的自适应预测控制策略

针对空调蒸发器运行状态,提出一种基于模糊神经模型的自适应单神经元预测控制器,该控制器具有结构简单、易于操作、控制器参数可在线调节的特点.离线建立空调蒸发器的模糊神经模型,再利用模型的梯度信息在线调节单神经元控制器参数,使控制系统较快地趋于稳定.仿真结果表明,提出的自适应单神经元预测控制器具有较好的动态性能和稳态性能,并能够成功地应用到空调蒸发器的控制中.     

A fuzzy controller improving a linear model following controllerfor motor drives

Since the dynamic response trajectory of a traditional fuzzy controller can not be quantitatively controlled, a fuzzy model following controller is proposed in this paper. In the proposed controller, an output feedback linear model following controller (LMFC) is first designed according to the roughly estimated plant model to let its response follow the output generated by a reference model. Then a model following error driven control signal is synthesized such that good model following characteristics can be preserved at various operating conditions. The proposed controller is applied to the speed control of an induction motor drive. Dynamic signal analysis of the model following behavior is made and the procedure for constructing the control algorithms is described in detail. The performance of the drive and the effectiveness of the proposed controller are demonstrated by some simulated and experimental results     

基于滤波器的局部自适应全变分图像去噪模型

综合利用冲击滤波器和非线性各向异性扩散滤波器对含噪图像做预处理,然后基于边缘检测函数建立反映图像局部特征的自适应权函数,构建能同时兼顾图像平滑去噪与边缘保留的局部自适应性的全变分模型,并建议用本原对偶算法快速求解。实验结果表明,同传统的全变分图像去噪模型相比,该局部自适应全变分模型在消除噪声的同时能很好地保持图像的边缘轮廓和纹理等细节特征,得到的复原图像在客观评价标准和主观视觉效果方面均有所提高。     

Superior step-size theorem and its application——Parallel variable step-size LMS filters algorithm

In several branches of adaptive filtering algorithms, the least mean square (LMS) algorithm is widely applied in many areas because of its low computational cost, good numerical stability and other features[1]. However, the contradiction between faster convergence and smaller steady-state mean squared error (MSE) affects its performance considerably. Step-size, as the key to the problem, can but offer only one choice of the two demands. Therefore, many variable step-size algorithms were prop…     

Superior step-size theorem and its application

With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construct a parallel variable step-size LMS filters algorithm. The theoretical model of the proposed algorithm is analyzed in detail. Simulations show the proposed theoretical model is quite close to the optimal variable step-size LMS (OVS-LMS) model. The experimental learning curves of the proposed algorithm also show the fastest convergence and fine tracking performance. The proposed algorithm is therefore a good realization of the OVS-LMS model.     

标准模型下基于身份代理盲签名方案

由于现有的基于身份代理盲签名方案要么没有得到形式化的安全证明,要么仅在随机预言（Random Oracle,RO）模型下可证明安全,提出一种标准模型下的基于身份代理盲签名方案。该方案的基本签名算法采用了Paterson等人提出的基于身份签名机制。在Paterson等人提出的标准安全模型基础上,引入代理签名敌手模型,并参考盲签名的安全模型,提出基于身份代理盲签名的标准模型。在此安全模型下,该方案被证明满足不可伪造性和盲性,具有可证明安全性。     

基于局部与全局拟合的活动轮廓模型

针对局部二值拟合(LBF)模型容易陷入能量泛函局部极小值的问题,提出基于局部与全局拟合的活动轮廓模型。引入一个衡量某点处局部区域内灰度分布是否均匀的特征函数,将LBF模型中的局部拟合项与CV模型中的全局拟合项相结合,同时保留LBF模型分割灰度不均匀图像和CV模型全局收敛性的优点。实验结果表明,该模型能够分割灰度不均匀图像,对初始轮廓的依赖性较弱,并且具有一定的抗噪性。     

A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization

In recent years, artificial neural networks (ANNs) have been commonly used for time series forecasting by researchers from various fields. There are some types of ANNs and feed forward neural networks model is one of them. This type has been used to forecast various types of time series in many implementations. In this study, a novel multiplicative seasonal ANN model is proposed to improve forecasting accuracy when time series with both trend and seasonal patterns is forecasted. This neural networks model suggested in this study is the first model proposed in the literature to model time series which contain both trend and seasonal variations. In the proposed approach, the defined neural network model is trained by particle swarm optimization. In the training process, local minimum traps are avoided by using this population based heuristic optimization method. The performance of the proposed approach is examined by using two real seasonal time series. The forecasts obtained from the proposed method are compared to those obtained from other forecasting techniques available in the literature. It is seen that the proposed forecasting model provides high forecasting accuracy.