Stochastic spectral unmixing with enhanced endmember class separation

Improvements to an algorithm for performing spectral unmixing of hyperspectral imagery based on the stochastic mixing model (SMM) are presented. The SMM provides a method for characterizing both subpixel mixing of the pure image constituents, or endmembers, and statistical variation in the endmember spectra that is due, for example, to sensor noise and natural variability of the pure constituents. Modifications of the iterative, expectation maximization approach to deriving the SMM parameter estimates are proposed, and their effects on unmixing performance are characterized. These modifications specifically concern algorithm initialization, random class assignment, and mixture constraints. The results show that the enhanced stochastic mixing model provides a better statistical representation of hyperspectral imagery from the perspective of achieving greater endmember class separation.     

基于PMOPSO方法的H型铲齿凸轮优化设计

给出一种H型铲齿凸轮的标准设计方法,用于解决多升程H型铲齿凸轮的优化设计问题。针对传统铲齿凸轮存在的过渡点冲击问题,提出基于H型凸轮从动件运动规律的铲齿凸轮设计方案。以凸轮面积为设计目标函数,以从动件的偏置量和初始位移为设计变量,在凸轮一般设计准则的基础上,考虑理论廓线曲率范围和压力角分布的约束条件,建立H型铲齿凸轮的优化设计模型。多升程H型铲齿凸轮的优化设计问题具有约束条件多、非线性强和计算复杂度高的特点,将多项式变异算子和标准粒子群优化结合,提出多项式变异粒子群优化方法。以此优化方法为基础,通过构造罚函数处理设计约束,分别求解三升程和四升程的H型铲齿凸轮优化设计问题。计算结果表明,提出的标准设计方法可显著降低多升程H型铲齿凸轮的工作轮廓面积,使铲削机构更加紧凑。     

基于混合粒子群优化算法辨识Hammerstein模型

非线性系统辨识是现代辨识领域中的一个主要问题。在非线性系统辨识中,系统常被表示为一系列块连接。针对非线性系统中的Hammerstein模型,本文提出了利用混合粒子群优化算法对非线性系统模型进行辨识。该方法的基本思想是将非线性系统的辨识问题转化为参数空间上的优化问题,然后采用粒子群优化算法(PSO)获得该优化问题的解。为了进一步增强粒子群优化算法的辨识性能,提出利用一种混合粒子群优化算法。最后,给出仿真实验,其结果验证了本文给出的辨识方法是有效的。     

基于多目标粒子群协同算法的状态参数优化

针对铅锌烧结过程综合透气性、烧结终点的优化具有强非线性、计算复杂等特点,提出了一种有效的多目标粒子群协同优化算法。首先,建立了有综合透气性、烧结终点两个目标的优化模型。接着,通过改进的约束比较方法、粒子极值选取方法,以及利用不同的粒子群来分别优化相应的变量,提出了一种改进的多目标粒子群协同优化算法。最后,利用提出的多目标优化算法进行综合透气性、烧结终点的优化。仿真结果表明,所提出的多目标优化算法能较好地解决综合透气性、烧结终点的优化问题。     

Combined particle swarm optimization and heuristic fuzzy inference systems for a smart home one-step-ahead load forecasting

This article presents a hybrid approach that combines particle swarm optimization (PSO) and heuristic fuzzy inference system (HFIS) for smart home one-step-ahead load forecasting. Smart home load forecasting is an important issue in the development of smart grids. Generally, the electricity consumption of a household is inherently nonlinear and dynamic and heavily dependent on the habitual nature of power demand, activities of daily living and on holidays or weekends, so it is often difficult to construct an adequate forecasting model for this type of load. To address this problem, a hybrid model, consisting of two phases, is proposed in this article. In the first phase, the popular PSO algorithm is used to determine the locations of fuzzy membership functions. Then, the proposed HFIS technique is used to develop the one-step-ahead load forecasting model in the second phase. Because of the robust nature of the proposed HFIS technique, which does not need to retrain or re-estimate model parameters, it is very suitable for smart home load forecasting. The proposed method was verified using two different households’ load data. Simulation results indicate that the proposed method produces better forecasting accuracy than existing methods.     

基于熵-流特征和樽海鞘群优化支持向量机的故障诊断方法

针对旋转机械设备故障特征提取困难的问题,提出一种熵-流特征和樽海鞘群优化支持向量机(salp swarm optimization support vector machine,SSO-SVM)的故障诊断方法。利用改进多尺度加权排列熵(improved multiscale weighted permutation entropy,IMWPE)提取机械设备不同工况下的故障特征;采用监督等度规映射(S-Isomap)流形学习进行降维处理,获取低维的熵-流特征集;将熵-流特征输入至SSO-SVM多故障分类器进行识别与诊断。行星齿轮箱故障诊断实验分析结果表明:IMWPE+S-Isomap熵-流特征提取方法优于现有的多尺度排列熵(multiscale permutation entropy,MPE)、多尺度加权排列熵(multiscale weighted permutation entropy,MWPE)和IMWPE等熵值特征提取方法以及IMWPE+等度规映射(Isomap)和IMWPE+线性局部切空间排列(linear local tangent space alignment,LLTSA)等熵-流特征提取方法;樽海鞘群算法对支持向量机参数寻优效果优于粒子群、灰狼群、人工蜂群和蝙蝠群等算法;所提故障诊断方法识别精度达到100%,能够有效诊断出行星齿轮箱各工况类型。     

基于GMPSO的有限元模型修正方法验证

为提高有限元模型修正方法效率,保证修正精度,提出基于高斯白噪声扰动的粒子群优化（GMPSO）有限元模型修正方法。介绍标准粒子群优化（PSO）方法和改进后的GMPSO方法,基于测试函数比对两种方法的全局寻优能力和寻优效率;提出高效的基于GMPSO有限元模型修正方法,阐述方法流程并明确各参数与实际物理量的对应关系;基于GMPSO有限元模型修正方法对高维有损伤简支梁模型（变量维度为10）实施修正,并与基于遗传算法（GA）的模型修正结果进行比对;基于GMPSO有限元模型修正方法对某在役桥梁结构实施修正（变量维度为13）,验证所提方法可行性。结果表明:经局部改进的GMPSO方法较原PSO方法的优化能力显著提升;高维损伤简支梁模型修正结果显示,基于GMPSO模型修正方法可获得较好的修正结果,修正效率较基于GA的模型修正方法有显著提升;在役桥梁结构有限元模型修正结果显示,基于GMPSO模型修正方法可有效降低主梁计算频率和试验频率的误差,所提方法可适用于较工程复杂结构模型修正问题。     

Modeling and identification of the multi-axis drive system using support vector machine and granular computing

Advanced manufacturing technology requires high-precision capability in multi-axis computer numerical control (CNC) machine tools. At present, the modeling and identification for the drive system of CNC machine tools has some defects. In order to solve the problem, some interdisciplinary theories and methods, such as support vector machines, granular computing, artificial immune algorithms, and particle swarm optimization algorithms, have been used to model and identify multi-axis drive systems for CNC machine tools. An identification method using a support vector machine, based on granular computing, is presented to identify a multi-axis servo drive system model for improving the precision of model identification, and an immune particle swarm optimization algorithm, based on crossover and mutation functions, is proposed to optimize the structure parameters of the support vector machine based on granular computing. The proposed identification method was evaluated by experiments using the multi-axis servo drive system. The experimental results showed that the proposed approach is capable of improving modeling and identification precision.     

基于粒子群优化算法的结构模型修改

结构模型修改已经演化为一个多学科的研究课题．在最优化框架内，应用了国际上最近提出的粒子群优化算法，该算法具有全局搜索能力并且不需要目标函数的解析表达式。对于一实际钢结构，利用部分和全部测量得到的模态数据进行了模型修改的实验研究．并与基于灵敏度分析、神经网络和遗传算法的模型修改方法进行了对比．以修改后模型计算出的模态数据与实验测得的模态数据的相似度来衡量模型修改的准确性。结果表明，在多数情况下，所提出的模型修改方法得到了最好的修改结果，因此，应用粒子群优化算法进行结构模型修改是可行的。     

Guided particle swarm optimization method to solve general nonlinear optimization problems

The development of hybrid algorithms is becoming an important topic in the global optimization research area. This article proposes a new technique in hybridizing the particle swarm optimization (PSO) algorithm and the Nelder–Mead (NM) simplex search algorithm to solve general nonlinear unconstrained optimization problems. Unlike traditional hybrid methods, the proposed method hybridizes the NM algorithm inside the PSO to improve the velocities and positions of the particles iteratively. The new hybridization considers the PSO algorithm and NM algorithm as one heuristic, not in a sequential or hierarchical manner. The NM algorithm is applied to improve the initial random solution of the PSO algorithm and iteratively in every step to improve the overall performance of the method. The performance of the proposed method was tested over 20 optimization test functions with varying dimensions. Comprehensive comparisons with other methods in the literature indicate that the proposed solution method is promising and competitive.     

复杂场景条件下的运动目标检测算法

针对复杂场景条件下运动目标检测方法存在的局限性,提出了一种基于运动检测和静止图像分割相融合的算法。采用相邻帧差法结合建立的假设检验模型进行自适应的运动目标检测;为消除孔径效应和噪声的影响,根据运动目标检测的结果,在当前帧利用区域增长法融合运动分割的结果。试验结果表明,算法能从复杂场景的图像序列中有效地检测和提取出运动目标,并有很强的鲁棒性。     

改进樽海鞘群优化K-means算法的图像分割

目的 针对樽海鞘群算法寻优精度低、易陷入到局部最优,以及K-means算法进行图像分割容易被初始聚类中心干扰等缺点,提出改进樽海鞘群优化K-means算法的图像分割。方法 首先利用Circle映射来对樽海鞘种群进行初始化;其次引入莱维飞行到领导者和追随者位置更新公式中,使得樽海鞘种群的多样性得到提高,克服算法陷入到局部最优。最后,对改进樽海鞘群算法先采用8个基准函数进行性能测试;再将改进樽海鞘群算法优化K-means进行图像分割。结果 改进算法在寻优精度、稳定性、收敛速度以及跳出局部最优的本领得到了提高。同时,改进樽海鞘群优化K-means算法进行图像分割,有效地提高了图像分割质量。结论 改进算法改善了原始樽海鞘群算法的寻优精度低、易陷入到局部最优的缺点,很好地优化了K-means算法对图像进行准确分割,在图像分割领域具有一定的参考意义。     

Hybrid approximation algorithm with Kriging and quadratic polynomial‐based approach for approximate optimization

This paper describes a new hybrid algorithm that uses a Kriging and quadratic polynomial‐based approach for approximate optimization. The Kriging method is used for generating a global approximation model, and the polynomial‐based approximation method is used for generating a local approximation model. The Kriging system is only used to construct a polynomial‐based locally approximate model by estimating some function values and Hessian components of an estimated surface. The number of Kriging estimations can be reduced in comparison with direct Kriging‐based optimization, and a local optimum solution on an approximated surface can be clearly estimated without use of an optimization procedure based on a local appropriate quadratic polynomial model. Numerical examples of engineering optimization using the proposed method illustrate validity and effectiveness of the proposed method. Copyright © 2006 John Wiley & Sons, Ltd.     

基于Wiener-Hammerstein模型的一种系统辨识方法

针对工业大系统中Wiener-Hammerstein模型,提出一种新的辨识方法。该方法结合分散辨识对线性系统辨识精度高的优点与混合粒子群优化解决非线性、不可微和多峰值的复杂问题的长处,进行复合控制,并利用计量学中的动态计量方法,建立动态计量仿真模型。仿真研究与实验结果表明,该方法应用在非线性分布参数系统辨识中可有效提高辨识精度。     

Swarm algorithms for single- and multi-objective optimization problems incorporating sensitivity analysis

Swarm algorithms such as particle swarm optimization (PSO) are non-gradient probabilistic optimization algorithms that have been successfully applied for global searches in complex problems such as multi-peak problems. However, application of these algorithms to structural and mechanical optimization problems still remains a complex matter since local optimization capability is still inferior to general numerical optimization methods. This article discusses new swarm metaphors that incorporate design sensitivities concerning objective and constraint functions and are applicable to structural and mechanical design optimization problems. Single- and multi-objective optimization techniques using swarm algorithms are combined with a gradient-based method. In the proposed techniques, swarm optimization algorithms and a sequential linear programming (SLP) method are conducted simultaneously. Finally, truss structure design optimization problems are solved by the proposed hybrid method to verify the optimization efficiency.     

Hybrid features and optimization-driven recurrent neural network for glaucoma detection

Glaucoma is considered as the main source of irrevocable loss of vision. The earlier diagnosis of glaucoma is essential to provide earlier treatment and to reduce vision loss. The fundus images are transfigured in the ophthalmology and are used to visualize the structures of the optic disc. However, accuracy is considered as a major constraint. To increase accuracy, an effective optimization-driven classifier is developed for glaucoma detection. The proposed Jaya-chicken swarm optimization (Jaya-CSO) is employed for training the recurrent neural network (RNN) for glaucoma detection. The proposed Jaya-CSO is designed by integrating the Jaya algorithm with the chicken swarm optimization (CSO) technique for tuning the weights of the RNN classifier. The method utilized optic disc features, statistical features, and blood vessel features for the determination of the glaucomatous region. The features obtained from the optic disc, blood vessels, and the fundus image is formulated as a feature vector. Finally, the glaucoma classification is done using RNN using the feature vector such that the RNN is trained using the proposed Jaya-CSO. The proposed Jaya-CSO outperformed other existing models with maximal accuracy of 0.97, the specificity of 0.97, and sensitivity of 0.97, respectively.     

Multiobjective crashworthiness optimization design of functionally graded foam-filled tapered tube based on dynamic ensemble metamodel

Foam-filled thin-walled structures have recently gained attention with increasing interest due to their excellent energy absorption capacity. In this study, a new type of foam-filled thin-walled structure called as functionally graded foam-filled tapered tube (FGFTT) is proposed. FGFTT consists of graded density foam and thin-walled tapered tube. In order to investigate the energy absorption characteristics of FGFTTs, the numerical simulations for two kinds of FGFTTs subjected to axial dynamical loading are carried out by nonlinear finite element code LS-DYNA. In addition, a new kind of multiobjective crashworthiness optimization method employing the dynamic ensemble metamodeling method together with the multiobjective particle swarm optimization (MOPSO) algorithm is presented. This new kind of multiobjective crashworthiness optimization method is then used to implement the crashworthiness optimization design of FGFTTs. Meanwhile, the crashworthiness optimization designs of FGFTTs are implemented by using traditional multiobjective crashworthiness optimization method, which employs metamodels such as polynomial response surface (PRS), radial basis function (RBF), kriging (KRG), support vector regression (SVR) or the ensemble with the static design of experiment (DOE). Finally, by comparing the optimal designs of FGFTTs obtained by using the new multiobjective crashworthiness optimization method and the traditional one, the results show that the proposed new crashworthiness optimization method is more feasible.     

基于粒子群算法的空间直线度误差评定

提出了一种满足最小区域法的空间直线度误差评价的新方法--粒子群算法。根据最小区域条件,建立了空间直线的数学模型以及优化目标函数。阐述了粒子群优化算法的原理和实现方法,然后根据粒子群算法优化求解。实例表明该方法对于空间直线度误差评定等非线性优化问题能得到最优解,可用于三坐标测量机等测量系统的空间直线度误差测量的数据处理。     

实测端元光谱和多光谱图像之间的模拟与细分

地物光谱特性是遥感应用的基础。本文以渭干河-库车河三角洲绿洲为研究区,首先选取裸土、植被两类地物作为研究对象,通过TM传感器的光谱响应函数,实现了将野外实测端元光谱拟合为多光谱离散光谱。其次在对TM图像的光谱波段进行细分的基础上,利用光谱知识库的数据支持来模拟获取具有更高光谱分辨率的细分光谱光学遥感图像,深入开展两种尺度相互转换的研究。结果表明:一、拟和的多光谱与TM像元光谱具有很好的相关性,在此基础上,采用线性算法建立端元光谱与遥感图像像元光谱的转换模型,实现了从实测端元光谱尺度向遥感多光谱像元尺度的定量光谱转换,为遥感定量分析奠定了一定基础。二、细分光谱模拟图像的方法能够较为可靠的模拟出真实高光谱分辨率图像的信息,模拟方法可信,达到了推广和验证的效果。     

Sequential linear programming and particle swarm optimization for the optimization of energy districts

This article deals with the optimization of energy resource management of industrial districts, with the aim of minimizing customer energy expenses. A model of the district is employed, whose optimization gives rise to a nonlinear constrained optimization problem. Here the focus is on its numerical solution. Two different methods are considered: a sequential linear programming method and a particle swarm optimization method. Efficient implementations of both approaches are devised and the results of the tests performed on several energetic districts are reported, including a real case study.