k中心点聚类算法在层次数据的应用

探讨了近年来提出的聚类概念与聚类过程、k中心点聚类的算法,在此基础上提出了一种基于层次数据模型的k中心聚类的改进算法.该算法一方面针对层次变量提出了相关的中值点概念;另一方面对传统k中心点算法进行了改进.最后对改进算法的复杂度进行了分析,由分析结果得出改进算法要比传统k中心点算法每次迭代耗费时间略少,但在总耗费时间上远远小于k中心点算法,大幅度提高了算法的整体性能.     

基于项的协同过滤在推荐系统中的应用研究

分析基于项的协同过滤在推荐系统中应用及所存在的问题,提出了一个基于项的协同过滤改进算法,并给出了改进算法在标准数据集上的实验结果,对改进算法与原算法进行了相关性能的比较分析,证明了改进算法的有效性.最后,对研究进行了总结,指出存在的不足,提出了进一步研究的方向.     

混合改进蚁群算法的函数优化

针对蚁群算法进化速度慢、容易出现停滞现象的不足,探讨了一种基于自适应信息素挥发因子的改进蚁群算法.针对蚁群算法容易陷入局部最优的缺点,提出了一种基于决策变量高斯变异的改进蚁群算法.针对蚁群算法速度慢的不足,探讨了一种基于决策变量边界自调整的改进蚁群算法.将上述3种改进相融合,提出了一种基于自适应信息素挥发因子、决策变量高斯变异和决策变量边界自调整3种改进策略的混合改进蚁群算法.将其应用于函数优化中,仿真结果表明,混合改进蚁群算法在收敛速度和收敛率方面都有很大改进,具有更好的寻优性能.     

基于改进粒子群优化算法的BP预测模型

该论文提出了基于改进粒子群优化的BP算法. 在该算法中,通过对粒子群优化算法中的惯性权重的计算方法的改进,同时利用改进的PSO算法替代了BP算法中的梯度下降算法,使得改进后的算法具有不易与陷入局部极小等优点. 并将该算法利用在预测气温上,实验证明： 改进后的算法在预测模型上能够取得较好的预测效果,提高预测精度.     

银行家算法的改进与实现

银行家算法是操作系统中避免死锁的经典算法,为提高系统的运行效率,文章对银行家算法进行了研究和改进.通过对银行家算法流程、算法优缺点的分析,以及对现有的银行家改进方法的讨论,提出一种银行家算法的改进思路.使用Java语言实现了改进的银行家算法,并运用Java多线程对操作系统中进程的同步进行模拟,验证了该算法的有效性.     

一种求解TSP问题的改进蚁群算法

针对基本蚁群算法存在收敛速度慢,易陷于局部最优解等缺点,提出了一种求解旅行商(TSP)问题的改进蚁群算法.通过在基本蚁群算法中提出保留最优解和引入个体差异策略的改进方法,有效地抑制了算法收敛过程中的停滞现象,提高了全局搜索能力和解的质量.TSPLIB的实例验证了该改进算法的有效性.     

一种新的可变步长LMS自适应滤波算法

针对LMS算法收敛性能差的缺点,提出一种改进LMS算法即NLMSISA.首先从理论上分析并介绍了LMS算法及其改进算法,并将改进算法-变符号函数法(ISA)和归一化变步长(NLMS)算法有机结合,提出了该NLMSISA算法,理论上该改进算法具有物理实现难度低、收敛速度快、且能收敛到更小且稳定的均方误差(MSE)的特点.在MATLAB下,通过采用LMS、LMSISA和NLMSISA三种算法对FIR和IIR两种结构的系统分别进行了辨识仿真研究,结合三种算法的均方误差收敛曲线分析,验证了提出的改进LMS算法在敛速度、稳态误差上都明显优于现有几种算法.该算法对于实际工程应用的自适应滤波问题提供了一个较好的算法.     

一种基于VxWorks的内存分配算法

研究了VxWorks系统内存分配算法,指出了常用内存管理算法的局限性,在此基础上,提出了一种改进的内存分配算法.改进的内存分配算法包括优化的内存块分配算法和快速高效的动态内存分配算法,两者结合使用将会有效提高嵌入式系统的性能.对改进内存算法的实现作了详细的介绍.     

运动矢量场自适应搜索算法的一种改进方案

在MVFAST算法的基础上,提出了一种改进的MVFAST算法.通过对MVFAST算法的分析,针对其不足之处,提出了MVFAST算法的改进算法.改进算法采用了高效的提前中止策略,即设置了动态的门限阈值,以及充分利用了视频序列的空间和时间相关性,对块进行运动类型划分,以采用不同的搜索策略对宏块进行起始点预测.该方法通过起始点预测、提前中止策略、对宏块进行划分,能够有效地处理视频序列.试验结果表明,在图像质量稍有提高的情况下,改进的算法能有效提高编码速度.     

粒子群算法研究与展望

介绍了基本粒子群算法,归纳了粒子群算法的研究现状和改进,包括:增加惯性因子的改进;基于收敛性分析的改进;导入其他演化算法思想的改进;建立非数值问题模型的改进.简要分析了PSO算法的应用.最后对PSO的研究现状做出总结和展望,提出未来的几个研究热点.     

The optimal projection equations for model reduction and the relationships among the methods of Wilson, Skelton, and Moore

First-order necessary conditions for quadratically optimal reduced-order modeling of linear time-invariant systems are derived in the form of a pair of modified Lyapunov equations coupled by an oblique projection which determines the optimal reduced-order model. This form of the necessary conditions considerably simplifies previous results of Wilson [1] and clearly demonstrates the quadratic extremality and nonoptimality of the balancing method of Moore [2]. The possible existence of multiple solutions of the optimal projection equations is demonstrated and a relaxation-type algorithm is proposed for computing these local extrema. A component-cost analysis of the model-error criterion similar to the approach of Skelton [3] is utilized at each iteration to direct the algorithm to the global minimum.     

改进的广义预测控制的稳定性分析

本文在广义预测控制(GPC)算法[1,2]的基础上提出了改进算法.并对改进的算法[4]进 行了稳定性分析,得到了简练的特征多项式,然后进一步证明其特征多项式中的参数可以直接 用预测模型、被控对象模型和控制参数代替,不必另行计算.其结论仍适用于原有的GPC算 法[1,2,5]的稳定性分析.     

Comparison of image recognition efficiency of Bayes,correlation, and modified Hopfield network algorithms

The statistical estimates of the probability of correct recognition of the images, noisy reference by an additive handicap, for Bayes, correlation, and modified Hopfield network algorithms are compared. It is shown that, in the case of complete a priori probability concerning a handicap, the modified Hopfield network algorithm reaches the quality of the Bayes algorithm. At a deviation a priori probability on a handicap, the quality of the Bayes algorithm is worse than that of the modified Hopfield network algorithm. The correlation algorithm is worse than the modified Hopfield network algorithm, in general.     

无线传感网数据信息的一种压缩算法

提出了一种改进型自适应Huffman编码算法,目的在于压缩传输数据的容量,该算法适用于内存和计算资源受限的无线传感网络节点。它与修剪树自适应Huffman编码算法lll相比较,能够更有效地利用内存空间,提供更好的压缩比。     

基于改进粒子群游优化的模糊逻辑系统自学习算法

Eberhart在［1］中提出了粒子群游优化算法（ParticleSwarmOptimizationAlgorithm）。该文将改进后的粒子群游算法应用于模糊逻辑系统自学习。模糊辨识器的计算机模拟证明了改进算法的有效性。     

Radial basis function networks, regression weights, and theexpectation-maximization algorithm

We propose a modified radial basis function (RBF) network in which the regression weights are used to replace the constant weights in the output layer. It is shown that the modified RBF network can reduce the number of hidden units significantly. A computationally efficient algorithm, known as the expectation-maximization (EM) algorithm, is used to estimate the parameters of the regression weights. A salient feature of this algorithm is that it decomposes a complicated multiparameter optimization problem into L separate small-scale optimization problems, where L is the number of hidden units. The superior performance of the modified RB network over the standard RBF network is illustrated by computer simulations     

An efficient fixed-lag smoothing algorithm for discrete linear systems

The modified Bryson-Frazier fixed interval smoothing algorithm [6], is an addendem to the Kalman filter. This algorithm when applied to the problem of fixed-lag smoothing is computationally more efficient than the algorithms recently reported in refs. [1–3]. Features of the algorithm are ease of implentation, computational efficiency, reduced storage requirements, and stability.     

A Modified Newton-Armijo Algorithm for Polynomial Smooth Support Vector Machine

Polynomial smooth vector machine (PSSVM) is an important model for classifying problems. It is an important work how to solve PSSVM more effectively. In this paper, a modified Newton-Armijo algorithm (MNAA) is proposed to solve PPSVM. It is shown by the theoretical analyses and numerical experiments that MNAA achieves a faster computation efficiency than classical Newton-Armijo algorithm (NAA). Furthermore, MNAA achieves a better computation efficiency obviously than that of [1], it is an improvement to the Newton-Armijo of [1].     

A modified gradient‐based algorithm for solving extended Sylvester‐conjugate matrix equations

In this paper, we present a modified gradient‐based algorithm for solving extended Sylvester‐conjugate matrix equations. The idea is from the gradient‐based method introduced in [14] and the relaxed gradient‐based algorithm proposed in [16]. The convergence analysis of the algorithm is investigated. We show that the iterative solution converges to the exact solution for any initial value based on some appropriate assumptions. A numerical example is given to illustrate the effectiveness of the proposed method and to test its efficiency and accuracy compared with those presented in [14] and [16].     

Batch estimation of a jump in the state of a stochastic linear system

A batch algorithm which estimates the state as well as a jump in the state which occurs at a known time is presented. The algorithm is modified such that the state estimate and the jump estimate are treated in a serial manner. A comparison with the estimator given in [1] and [2] is included.