Quasi gradient-based inversion-free iterative algorithm for solving a class of the nonlinear matrix equations

Inspired by the gradient-based and inversion-free iterations, a new quasi gradient-based inversion-free iterative algorithm is proposed for solving the nonlinear matrix equation $X+A^{\mathrm{T}}{X}^{?n}A=I$. The convergence proof of the suggested algorithm is given. Several matrix norm inequalities are established to depict the convergence properties of this algorithm. Three numerical examples are given to illustrate the effectiveness of the suggested algorithms.     

最优化算法与纯元素谱剥离相结合的能量色散X射线荧光谱解谱方法

在进行能量色散X射线荧光光谱(EDXRF)解谱时,如果给定样品的元素构成,采用纯元素谱图剥离的方式会更加合理,但由于每个元素的谱线都不止一条,如果仅仅针对主线对齐做剥离,会导致结果的严重失真,同时剥离次序对解谱结果有较大影响。介绍了一种最优化算法与纯元素谱剥离相结合的EDXRF解谱方法,该方法以多个纯元素谱的强度大小和峰位漂移道数作为变量构建残差方程,并用最优化计算的方法去调整,直至残差达到极小。实验采用强度顺序剥离、能量顺序剥离和最优化算法拟合3种方法分别对谱线重叠较为严重的La-Ce-Pr-Nd混合液样品的L系谱线进行了解析,结果表明最优化计算方法拟合的谱图与原始谱的残差 (1415.0)比另两种方法(166094.0和3192.7)大幅度缩小,拟合谱与原始谱更为吻合,并且方法在实现时对初值不敏感,解谱精度也不依赖于剥离次序。     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

能源土边界面模型的应力更新算法及算例分析

水合物的存在会显著影响能源土的刚度、峰值强度与剪胀性。针对已有能源土模型的不足，结合边界面模型的建模思想，构建一个新的能源土边界面模型，模型参数较少，能够恰当反映能源土的应力-应变关系。计算能源土变形问题的核心在于正确积分塑性本构方程，应用完全隐式回退Euler算法，建立模型应力及塑性内变量的更新公式，并给出显式的一致性切线模量表达式。基于ABAQUS软件提供的二次开发接口，编写模型的用户材料子程序，应用已有试验数据验证程序正确性。最后应用开发的子程序对能源土的平面应变试验进行模拟，分析水合物饱和度对剪切带倾角与孔隙比的影响。     

应用膨胀算法提取目标的直接定位算法

为了解决被动雷达系统中的多发射源定位问题，提出了一种基于多重信号分类(MUSIC)算法和图像膨胀(IE)算法的直接定位方法。该方法结合了谱分析中的MUSIC思想，通过对接收量测协方差矩阵进行特征分析求解目标的位置。首先，在目标个数未知的前提下，利用Akaike信息准则(AIC)来确定模型阶数；然后，推导了基于MUSIC的定位代价函数；之后，利用图像膨胀算法处理得到的代价函数平面；最后，膨胀处理后的输出为目标个数及目标位置的估计值。提出的算法有效地解决了目标检测及提取的问题，能够确定多个目标的位置坐标，为后续的定位性能分析提供可能性，也保证了算法的完整性。进一步地分析了多个临近目标情况下影响目标提取性能的主要因素。     

基于粒子群优化和支持向量机的花粉浓度预测模型

为了提高花粉浓度预报的准确率，解决现有花粉浓度预报准确率不高的问题，提出了一种基于粒子群优化（PSO）算法和支持向量机（SVM）的花粉浓度预报模型。首先，综合考虑气温、气温日较差、相对湿度、降水量、风力、日照时数等多种气象要素，选择与花粉浓度相关性较强的气象要素构成特征向量；其次，利用特征向量与花粉浓度数据建立SVM预测模型，并使用PSO算法找出最优参数；然后利用最优参数优化花粉浓度预测模型；最后，使用优化后的模型对花粉未来24 h浓度进行预测，并与未优化的SVM、多元线性回归法（MLR）、反向神经网络（BPNN）作对比。此外使用优化后的模型对某市南郊观象台和密云两个站点进行逐日花粉浓度预测。实验结果表明，相比其他预报方法，所提方法能有效提高花粉浓度未来24 h预测精度，并具有较高的泛化能力。     

大规模生物网络马尔可夫聚类的并行化算法

马尔可夫聚类算法（MCL）是在大规模生物网络中寻找模块的一个有效方法，能够挖掘网络结构和功能影响力较大的模块。算法涉及到大规模矩阵计算，因此复杂度可达立方阶次。针对复杂度高的问题，提出了基于消息传递接口（MPI）的并行化马尔可夫聚类算法以提高算法的计算性能。首先，生物网络转化成邻接矩阵；然后，根据算法的特性，按照矩阵的规模判断并重新生成新矩阵以处理非平方倍数矩阵的计算；其次，并行计算通过按块分配的方式能够有效地实现任意规模矩阵的运算；最后，循环并行计算直至收敛，得到网络聚类结果。通过模拟网络和真实生物网络数据集的实验结果表明，与全块集体式通信（FCC）并行方法相比，平均并行效率提升了10个百分点以上，因此可以将该优化算法应用在不同类型的大规模生物网络中。     

Modification of the Wu-Mendel approach for linguistic summarization

This paper presents versatile modification of the Wu-Mendel approach for linguistic summarisation and concentrates on eliminating the following main drawbacks of the approach: significant user/expert involvement, significant and rapidly increasing computational cost, some flaws in quality measures formulae. The techniques and formulae proposed to eliminate the aforesaid drawbacks are put through the experimental verification process involving several real-world datasets. The results of the experimental verification demonstrate the increased efficiency and effectiveness of the modified Wu-Mendel approach for linguistic summarisation.     

Optimal interval type-2 fuzzy fractional order super twisting algorithm: A second order sliding mode controller for fully-actuated and under-actuated nonlinear systems

In this paper, a novel interval type-2 fuzzy fractional order super twisting algorithm (IT2FFOSTA) which is essentially a second order sliding mode controller is presented. The proposed IT2FFOSTA enhances fractional order super twisting algorithm (FOSTA) by taking advantage of an interval type-2 fuzzy fractional order sliding surface (IT2FFOSS) for some classes of fully-actuated and under-actuated nonlinear systems in presence of uncertainty. The FOSTA significantly reduces the amount of chattering and the IT2FFOSS results in decreasing the tracking error, control effort, and chattering level. In order to control under-actuated systems, a hierarchical sliding surface is employed. The multi-tracker optimization algorithm is utilized to adjust the controller’s parameters; this leads to an optimal performance for the IT2FFOSTA. To examine the performance of the IT2FFOSTA, some simulation and experimental tests on three examples of different classes of fully-actuated and under-actuated systems, including ball and plate, inverted pendulum, and ball and beam systems are carried out. The simulation and experimental results demonstrate the superiority of the IT2FFOSTA in reducing the amount of chattering, tracking error, and control effort compared to those of the other control methods.     

采用信息增益率的混合入侵检测模型设计

针对现有混合入侵检测模型仅定性选取特征而导致检测精度较低的问题，同时为了充分结合误用检测模型和异常检测模型的优势，提出一种采用信息增益率的混合入侵检测模型.首先，利用信息增益率定量地选择特征子集，最大程度地保留样本信息；其次，采用余弦时变粒子群算法确定支持向量机参数构建误用检测模型，使其更好地平衡粒子在全局和局部的搜索能力，然后，选取灰狼算法确定单类支持向量机参数构建异常检测模型，以此来提高对最优参数的搜索效率和精细程度，综合提高混合入侵检测模型对攻击的检测效果；最后，通过两种数据集进行仿真实验，验证了所提混合入侵检测模型具有较好的检测性能.