基于改进粒子群算法的模糊聚类-概率神经网络模型的企业财务危机预警模型研究

为了充分发挥概率神经网络在企业财务危机预警中的作用,克服概率神经网络平滑参数难以确定和空间复杂度高的不足,本文提出一类新的参数动态调整的粒子群算法优化概率神经网络的平滑参数,进而采用改进粒子群算法优化初始隶属度矩阵的模糊聚类方法实现对样本的选择,解决了概率神经网络平滑参数的确定及空间结构复杂的问题。提出了基于改进粒子群算法的模糊聚类-概率神经网络企业财务危机预警模型,并以我国上市公司作为研究对象进行了实证研究。结果表明,经过模糊聚类和改进粒子群算法优化的概率神经网络具有更优的预测性能,并在企业财务危机长期预警方面具有一定效用。     

PageRank的改进算法——调整阻尼因子

Google将PageRank定义成某个非周期不可约Markov转移概率矩阵的平稳分布,于是对PageRank算法的改进所得到的矩阵一定要是非周期不可约Markov转移概率矩阵,结合RageRank算法和林共进修正算法思想,以及修正算法存在的问题,本文给出了改进算法,并通过简单试验对改进算法进行调整,调整后的改进算既满足Google的初衷又解决其算法的问题,也没有增加算法的复杂度.     

一种新城市气温模式分类的聚类算法

城市气温是对城市气候特性评价的一个重要指标.提出核概率聚类算法并将其应用于城市气温的模式分类中,以此寻找城市发展上的共同点.该算法在概率聚类算法上引入了核学习方法的思想,能够很好地处理噪音和孤立点,实现更为准确的聚类.实验结果表明,与相关聚类算法相比,核概率聚类算法聚类效果好,且算法能够很快地收敛.     

一种新的混合蚁群算法

设计一种新的混合蚁群算法,该算法以一种新的加权二进制蚁群算法为基础,将分布估计算法PB IL的概率分布模型用来指导蚂蚁路径的选择,同时对不同位置的蚂蚁采用加权系数来控制信息素散发量,根据信息素得到的转移概率、PB IL的模型概率及二者融合的概率来产生新的个体,保证了个体的多样性,从而提高了算法的快速性和全局最优解的搜索能力.通过测试函数优化表明该算法具有良好的收敛速度和稳定性,改善了蚁群算法容易陷入局部最优而早熟的缺陷.     

一种基于后验概率判决的one-by-one快速相关攻击算法(英文)

本文描述了一种基于后验概率判决的 one by one快速相关攻击算法.本文试图通过概率的观点来看待和分析快速相关攻击问题.该算法的优点有以下三点.首先,和文献[5]相比 one by one算法减少了对存储空间的需求.其次,提出了攻击失败概率的概念,并利用中心极限定理给出了它和密钥流序列长度的关系.最后,和文献[4]相比,该算法只需要更少的密钥流序列就可以达到几乎相同的攻击效果.     

求解随机凸规划概率约束问题的对偶算法

1引言随机规划中的概率约束问题在工程和管理中有广泛的应用.因为问题中包含非线性的概率约束,它们的求解非常困难.如果目标函数是线性的,问题的求解就比较容易.给出了一个求解随机线性规划概率约束问题的综述.原-对偶算法和切平面算法是比较有效的.在本文中,我们讨论随机凸规划概率约束问题:     

相关导航系统捕获概率的解析研究与参量分析

本文纠正了[2]中相关导航系统乘积相关算法(COR)捕获概率方程中的错误。推导出另一重要算法——平均方差算法(MSD)的捕获概率方程。并且给出了估计精度较高的COR、MSD和MAD(平均绝对差)三种算法的改进型捕获概率方程。     

寿险中的破产理论及应用

本文研究了求解寿险中破产概率的简洁方法 ,得到寿险破产模型 ,设计了求解寿险中的破产概率的一种算法 ,并得到寿险破产概率的一个上界。     

基于有效Monte Carlo模拟的外汇期权组合非线性VaR模型

为了克服极小概率事件发生概率估计的困难,提出了把重要抽样技术发展到外汇期权组合非线性VaR模型中,估计出组合损失概率。为了进一步达到减少模拟估计误差目的,在重要抽样技术基础上使用分层抽样技术,进行更有效的Monte Carlo模拟。数值结果表明,重要抽样技术算法比常用Monte Carlo模拟法的计算效率更有效;而重要抽样技术和分层抽样技术相结合算法比重要抽样技术算法更有效地减少模拟所要估计的组合损失概率的方差,有着更高的计算效率。     

基于IEEE802.16e的退避算法研究与改进

对IEEE802.16e的退避算法进行了研究,分析了该算法的不足,并进行了改进,提出了基站可随冲突MS数目动态调整起始退避窗大小的改进退避算法,给出了两种算法的仿真实验结果,证明了改进的算法可大大提高系统接入的性能,降低了接入冲突的概率.     

椭圆边值问题的概率算法

本文重新建立了椭圆边值问题的概率模型,在Ｍｏｎｔｅ－Ｃａｒｌｏ算法的基础上,引入了一种新的高精度概率算法,取得很大进展．     

An anytime deduction algorithm for the probabilistic logic and entailment problems

We study two basic problems of probabilistic reasoning: the probabilistic logic and the probabilistic entailment problems. The first one can be defined as follows. Given a set of logical sentences and probabilities that these sentences are true, the aim is to determine whether these probabilities are consistent or not. Given a consistent set of logical sentences and probabilities, the probabilistic entailment problem consists in determining the range of the possible values of the probability associated with additional sentences while maintaining a consistent set of sentences and probabilities.This paper proposes a general approach based on an anytime deduction method that allows the follow-up of the reasoning when checking consistency for the probabilistic logic problem or when determining the probability intervals for the probabilistic entailment problem. Considering a series of subsets of sentences and probabilities, the approach proceeds by computing increasingly narrow probability intervals that either show a contradiction or that contain the tightest entailed probability interval. Computational experience have been conducted to compare the proposed anytime deduction method, called ad-psat with an exact one, psatcol, using column generation techniques, both with respect to the range of the probability intervals and the computing times.     

On simulating the quantum and classical branching programs

The complexity classes defined on the basis of branching programs are considered. Some basic relations are established between the complexity classes defined by the probabilistic and quantum branching programs (measure-once, as well as measure-many), computing with bounded or unbounded error. To prove these relations, we developed a method of “linear simulation” of a quantum branching program and a method of “quantum simulation” of a probabilistic branching program.     

The inverse problem of the scatter of unguided missiles

The inverse scatter problem determines the characteristics of the perturbations of the initial conditions, design parameters, and the wind velocity from given characteristics of the scatter of the point of impact of an unguided missile. A deterministic and a probabilistic formulation of the problem are considered. Some properties of its solutions are noted and a method of computing them is proposed.     

On computation of the greatest common divisor of several polynomials over a finite field

We propose a probabilistic algorithm to reduce computing the greatest common divisor of m polynomials over a finite field (which requires computing m−1 pairwise greatest common divisors) to computing the greatest common divisor of two polynomials over the same field.     

Exploiting independencies to compute semigraphoid and graphoid structures

We deal with conditional independencies, which have a fundamental role in probability and multivariate statistics. The structure of probabilistic independencies is described by semigraphoids or, for strictly positive probabilities, by graphoids. In this paper, given a set of independencies compatible with a probability, the attention is focused toward the problem of computing efficiently the closure with respect to the semigraphoid and graphoid structures. We introduce a suitable notion of projection in order to provide a new method which properly uses conditional independence statements.     

Nonmonotonic probabilistic logics under variable-strength inheritance with overriding: Complexity,algorithms, and implementation

In previous work, I have introduced nonmonotonic probabilistic logics under variable-strength inheritance with overriding. They are formalisms for probabilistic reasoning from sets of strict logical, default logical, and default probabilistic sentences, which are parameterized through a value λ ∈ [0, 1] that describes the strength of the inheritance of default probabilistic knowledge. In this paper, I continue this line of research. I give a precise picture of the complexity of deciding consistency of strength λ and of computing tight consequences of strength λ. Furthermore, I present algorithms for these tasks, which are based on reductions to the standard problems of deciding satisfiability and of computing tight logical consequences in model–theoretic probabilistic logic. Finally, I describe the system nmproblog, which includes a prototype implementation of these algorithms.     

用概率论计算质谱的氢同位素相对丰度

通过同位素气体原子之间相互碰撞的概率计算,导出相应同位素离子的形成及其质谱分布,从而找到质谱分析的计算公式.     

Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA

Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, model-based clustering techniques usually perform poorly when dealing with high-dimensional data streams, which are nowadays a frequent data type. To overcome this limitation of model-based clustering, we propose an online inference algorithm for the mixture of probabilistic PCA model. The proposed algorithm relies on an EM-based procedure and on a probabilistic and incremental version of PCA. Model selection is also considered in the online setting through parallel computing. Numerical experiments on simulated and real data demonstrate the effectiveness of our approach and compare it to state-of-the-art online EM-based algorithms.     

Lifting and recombination techniques for absolute factorization

In the vein of recent algorithmic advances in polynomial factorization based on lifting and recombination techniques, we present new faster algorithms for computing the absolute factorization of a bivariate polynomial. The running time of our probabilistic algorithm is less than quadratic in the dense size of the polynomial to be factored.