基于alpha稳定分布的盲信号分离

针对当前重拖尾信号盲分离的存在问题,提出一种基于alpha稳定分布的重拖尾信号在线盲信号分离算法。离线计算标准alpha稳定分布的概率密度函数,建立标准函数库查找表;在线估计信号的alpha稳定分布的特征参数、对称参数和尺度参数,从而可以快速获得信号的概率密度函数;采用多层神经网络准确估计评价函数。仿真结果表明,该算法具有较好的分离性能和较低的计算复杂度。     

基于预处理提高角度频率联合估计的稳健性

研究了可建模为对称α稳定（SαS）分布的冲击噪声环境中的联合角度频率估计问题。利用SαS分布的特性，首先对阵列接收数据做高斯拖尾零记忆非线性变换（GZMNL）进行预处理，以降低幅值大的异常数据对参数估计性能的影响，然后给出了用2-D ESPRIT型方法来联合估计信号的二维参数。计算机仿真结果显示了所给算法的有效性及其对冲击噪声环境的稳健性。     

基于均差滤波与高斯和的非线性非高斯系统滤波算法

针对一类非线性非高斯系统的滤波问题,在分析均差滤波算法和高斯和滤波算法的基础上,提出一种基于均差滤波的高斯和滤波算法,适于处理非线性非高斯系统的滤波问题.对于似然密度位于条件转移概率密度拖尾处的情况,与传统的粒子滤波算法相比,所提算法能提高滤波的精度和实时性.仿真实验验证了新算法的有效性.     

基于核密度最大熵方法的杂系混合信号盲分离*

传统的独立分量分析方法普遍存在的非线性评价函数只能凭经验选取,当混合信号同时包含超高斯和亚高斯信号时,算法难以取得很好的分离效果。利用基于随机变量矩的核密度最大熵方法对非线性函数进行直接估计,提出了基于核密度最大熵方法的杂系混合信号盲分离算法,成功地分离了杂系混合信号。仿真结果验证了算法的有效性。     

一种改进牛顿法的盲源分离算法

针对盲信号分离中超高斯信号亚高斯信号混叠难以分离的问题,提出一种基于改进牛顿法的盲源分离算法.该方法引入开关准则,利用随机变量的峭度来区分信号的类型,不同的信号选择不同的非线性函数,通过牛顿迭代方法求出分离矩阵,实现同时含有超高斯信号和亚高斯信号的杂系混合信号的盲源分离.仿真实验表明了该方法计算量小,易于实现,对于杂系...     

非线性系统中稳定有色噪声建模及FLOC谱特性

用多项式自回归模型对非线性系统中稳定有色噪声建模,利用扩展的迭代重加权最小[p]范数算法进行模型参数估计。系统研究了分数低阶协方差谱的性质,并对无限方差非高斯多项式自回归有色噪声进行频域特性分析。理论分析和仿真实验表明,EIRLP算法是在高斯和分数低阶稳定分布噪声条件下具有良好韧性的参数估计方法。仿真通过对稳定有色噪声条件下的正弦信号进行谱估计,结果表明,分数低阶协方差谱具有良好韧性的谱估计性能。     

基于高斯–广义双曲混合分布的非线性卡尔曼滤波

本文研究带非平稳厚尾非高斯量测噪声的非线性系统状态估计问题.考虑到广义双曲分布包含多种常见厚尾分布特例,且其混合分布为共轭的广义逆高斯分布,选用广义双曲分布建模厚尾噪声;进而引入伯努利变量构建高斯–广义双曲混合分布来建模非平稳厚尾噪声,并利用该分布的高斯分层结构得到系统的概率模型.随后采用变分贝叶斯方法实现对系统状态以及噪声参数的后验估计,得到针对此类噪声系统的卡尔曼滤波(Kalman filter, KF)框架,现有的几种鲁棒滤波算法均是本文算法的特例.机器人跟踪仿真实验表明,所提算法与同类算法相比具有更好的估计精度和数值稳定性,且对于初始参数具有较好的鲁棒性.     

对称α稳定分布噪声下的软解映射算法

对称α稳定(SαS)分布噪声是一种非高斯噪声,相对于高斯噪声具有明显的脉冲特性,因此高斯噪声下的软解映射算法不适用于SαS分布噪声中。为解决该问题,根据高斯噪声下软解映射算法的对数似然比和信号幅度呈线性的特点,提出一种SαS分布噪声下基于欧式距离的软解映射算法,只需在高斯噪声下的软解映射算法和译码算法之间加入预处理算法,限制比特软信息的幅度,并将幅度过高的软信息置零。仿真结果显示,该算法实现简单、运算量低,所需信噪比在α=1.84的SαS分布噪声下比Huber算法低0.3 dB,在α=1.3的SαS分布噪声下低2 dB~5 dB。     

基于邻域场拉普拉斯混合模型图像分割的研究

针对高斯混合模型（GMM）不能有效处理重尾噪声下图像拖尾情况,提出了基于拉普拉斯（Laplacian）分布的有限混合模型图像分割方法。与标准拉普拉斯混合模型（LMM）将像素点作为孤立个体不同的是,该方法充分考虑了相邻像素点间的空间关系。相较传统混合模型参数估计采用的EM算法,该方法采用梯度下降法优化参数。实验结果表明在处理重尾噪声时,该方法与标准LMM算法和GMM算法相比,鲁棒性更好,分割更精确有效。     

基于负熵最大化改进的语音音乐信号分离

负熵是一种重要的非高斯性度量方法,最大化负熵使随机变量的非高斯性达到最大,从而使输出的各分量之间相互独立。负熵最大化算法以负熵作为目标函数,牛顿迭代法作为优化算法,针对牛顿迭代法中对初始值选择敏感的问题,用牛顿下山法代替牛顿迭代法,通过改变下山因子,使目标函数呈下降趋势,降低算法对初始值的依赖性。实验结果表明,改进后的算法在不同初始值下均能较好地分离语音音乐混合信号,改善了初值敏感问题。     

Fuzzy random multi-objective optimization based routing for wireless sensor networks

Wireless sensor networks are deployed in complex and uncertain environments, and multiple objectives of routing algorithms are expected to be optimal. However, routing algorithms based on deterministic single objective optimization may not flexibly meet the above needs of applications. This paper adopts fuzzy random optimization and multi-objective optimization, introduces fuzzy random variables to describe both fuzziness and randomness of link delay, link reliability and nodes’ residual energy, and proposes a routing model based on fuzzy random expected value and standard deviation model. A hybrid routing algorithm based on fuzzy random multi-objective optimization is designed, which embeds fuzzy random simulation into genetic algorithm with Pareto optimal solution. Simulation results show that the presented algorithm, by adjusting the parameters of fuzzy random variables for depicting both fuzziness and randomness, achieves a longer lifetime and wider performances of delay, latency jitter, reliability, communication interference, energy and balanced energy distribution. Therefore, the presented algorithm can meet different application needs of the cluster head network in the two-tiered wireless sensor networks.     

A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems

The Markowitz’s mean-variance (M-V) model has received widespread acceptance as a practical tool for portfolio optimization, and his seminal work has been widely extended in the literature. The aim of this article is to extend the M-V method in hybrid decision systems. We suggest a new Chance-Variance (C-V) criterion to model the returns characterized by fuzzy random variables. For this purpose, we develop two types of C-V models for portfolio selection problems in hybrid uncertain decision systems. Type I C-V model is to minimize the variance of total expected return rate subject to chance constraint; while type II C-V model is to maximize the chance of achieving a prescribed return level subject to variance constraint. Hence the two types of C-V models reflect investors’ different attitudes toward risk. The issues about the computation of variance and chance distribution are considered. For general fuzzy random returns, we suggest an approximation method of computing variance and chance distribution so that C-V models can be turned into their approximating models. When the returns are characterized by trapezoidal fuzzy random variables, we employ the variance and chance distribution formulas to turn C-V models into their equivalent stochastic programming problems. Since the equivalent stochastic programming problems include a number of probability distribution functions in their objective and constraint functions, conventional solution methods cannot be used to solve them directly. In this paper, we design a heuristic algorithm to solve them. The developed algorithm combines Monte Carlo (MC) method and particle swarm optimization (PSO) algorithm, in which MC method is used to compute probability distribution functions, and PSO algorithm is used to solve stochastic programming problems. Finally, we present one portfolio selection problem to demonstrate the developed modeling ideas and the effectiveness of the designed algorithm. We also compare the proposed C-V method with M-V one for our portfolio selection problem via numerical experiments.     

A new approach to design safe-supported HDD against random excitation by using optimization of rubbers spatial parameters

Hard disk drives (HDDs) of laptop personal computers (LPCs) are devices vulnerable in harsh mechanical environments. Hence, they need to be protected against damages due to vibration in order to have better read/write performance. In the present study, a LPC and its HDD are modeled as a system with two degrees of freedom and the nonlinear optimization method is employed to perform a passive control through minimizing the root mean square of HDD absolute acceleration due to a base random excitation. The presented random excitation is considered as a stationary, zero mean process with Gaussian distribution. In addition, eleven inequality constraints are defined based on geometrical limitations and allowable intervals of lumped modal parameters. The target of the optimization is to obtain optimum modal parameters of rubber mounts and rubber feet as design variables and subsequently propose new characteristics of rubber mounts and rubber feet to be manufactured for HDD protection against random excitation. In this paper, a nonlinear optimization problem is separately solved for three widely-used cases of HDD by using modified constrained steepest descent algorithm (PLBA) which was extended based on sequential quadratic programming. Finally, the genetic algorithm is used to verify results of the PLBA algorithm.

     

A Computational Approach to Finding Causal Economic Laws

This paper states four realities of econometric model buildingand shows that an econometric model can be causal only if theinterpretations given to its coefficients are consistent withthese realities. A numerically stable algorithm for estimatingsuch a model subject to equality and inequality constraints onthe model parameters is presented. This algorithm is designed insuch a way that it can be applied even when the matrix ofobservations on the model's independent variables and thecovariance matrix of the model's errors are deficient in rank.     

Stochastic simulation-based genetic algorithm for chance constraint programming problems with continuous random variables

In this article, we present a stochastic simulation-based genetic algorithm for solving chance constraint programming problems, where the random variables involved in the parameters follow any continuous distribution. Generally, deriving the deterministic equivalent of a chance constraint is very difficult due to complicated multivariate integration and is only possible if the random variables involved in the chance constraint follow some specific distribution such as normal, uniform, exponential and lognormal distribution. In the proposed method, the stochastic model is directly used. The feasibility of the chance constraints are checked using stochastic simulation, and the genetic algorithm is used to obtain the optimal solution. A numerical example is presented to prove the efficiency of the proposed method.     

基于随机森林的短期电量负荷精准预测方法

可再生能源并入电网后,电能供给量增加,短期电量负荷情况难以预测,无法制定准确的电能分配策略,由此,提出基于随机森林的短期电量负荷精准预测方法研究。深入分析短期电量负荷预测影响因素（气象、时间、电价与随机干扰因素）,选取适当的模型输入变量（历史电量负荷数据、温度数据与日类型）,结合随机森林算法构建短期电量负荷预测模型,并重复确定相似日的选取规则,采用粒子群优化算法寻找预测模型参数最佳值,将样本集输入至模型中,获得精准的短期电量负荷预测结果。实验数据显示:当输入变量数量达到一定值后,应用提出方法获得的短期电量负荷预测时延稳定在0.55s左右,短期电量负荷预测误差几乎为0,充分证实了提出方法应用性能较佳。     

An improved two-stage framework of evidence-based design optimization

In this paper, an improved two-stage framework is presented to handle the evidence-based design optimization (EBDO) problem under epistemic uncertainty. The improvements include two aspects: (1) in the first stage, the equal areas method is employed to transform evidence variables into random variables, which avoids the assumption that unknown evidence variables and parameters obey the normal distribution. Then, a reliability-based design optimization (RBDO) problem with random variables is defined and solved by the sequential optimization and reliability assessment (SORA) method; (2) in the second stage, an improved algorithm is presented, which can calculate the plausibility of constraint violation more efficiently by continuously recording the minimum and maximum values of limit-state functions. The computational accuracy and efficiency of the improved framework are tested by numerical and engineering examples.     

An improved quantum-behaved particle swarm optimization and its application to medical image registration

This paper investigates the quantum-behaved particle swarm optimization (QPSO) algorithm from the perspective of estimation of distribution algorithm (EDA) which reveals the reason of QPSO's superiority. A revised QPSO (RQPSO) technique with a novel iterative equation is also proposed. The modified technique is deduced from the distribution function of the sum of two random variables with exponential and normal distribution, respectively. We present a diversity-controlled RQPSO (DRQPSO) algorithm, which helps prevent the evolutionary algorithms’ tendency to be easily trapped into local optima as a result of rapid decline in diversity. Both the RQPSO and DRQPSO are tested on three benchmark functions, as well as in medical image registration for performance comparison with the particle swarm optimization and QPSO.     

Uncertain linear regression model and its application

Regression analysis is a statistical process for estimating the relationships among variables based on probability. Because not all the imprecise quantities can be described by random variables, it is necessary to investigate relationships between an uncertain variable and some other variables. In this paper, an uncertain linear regression model is established based on uncertainty theory. Then, the estimators of parameters are obtained in the proposed model by the empirical uncertainty distribution coming from experts’ experimental data. Finally, the uncertain linear regression model is applied to solve an estimate problem.     

Some properties of fuzzy random renewal processes

Fuzzy random variable is a measure function from a probability space to a collection of fuzzy variables. Based on the fuzzy random theory, this paper addresses some properties of fuzzy random renewal processes generated by a sequence of independent and identically distributed (iid) fuzzy random interarrival times. The relationship between the expected value of the fuzzy random renewal variable and the distribution functions of the /spl alpha/-pessimistic values and /spl alpha/-optimistic values of the interarrival times is discussed. Furthermore, the fuzzy random style of renewal equation is provided. Finally, fuzzy random Blackwell's renewal theorem and Smith's key renewal theorem are also given.