Fuzzy hyperlattices

In this paper, we introduce the notion of fuzzy hyperlattices and establish connections between fuzzy hyperlattices and hyperlattices. Then we discuss the relations between the weak homomorphism of fuzzy hyperlattices and the weak homomorphism of corresponding hyperlattices. Moreover, we define and analyze the hypercongruence on hyperlattices and the fuzzy (strong) hypercongruence on fuzzy hyperlattices. And we study the interrelation between the (fuzzy) hypercongruence on (fuzzy) hyperlattices and the homomorphism of (fuzzy) hyperlattices. Also, a relation between the fuzzy hypercongruence on fuzzy hyperlattices and the hypercongruence on corresponding hyperlattices is obtained. Finally, we apply the hypercongruence and the fuzzy strong hypercongruence to construct quotient structures of hyperlattices and fuzzy hyperlattices, respectively.     

基于完备抽象解释的模型检验CTL公式研究

在模型检验中,抽象是解决状态空间爆炸问题的重要方法之一.给定具体Kripke结构和时序描述语言CTL,基于抽象解释框架以及完备抽象解释和性质强保留之间的关系,抽象模型最小精化使得CTL性质强保留,可转换为抽象解释中抽象域的最小完备精化,并且总是存在抽象域的最小完备精化.根据状态标签函数确定初始抽象域,然后通过不动点求解,获得对CTL标准算子完备的最小抽象域,并依据此抽象域求得CTL性质强保留的最优抽象状态划分,最后构造出CTL性质强保留且最优的抽象状态转换系统.并指出了抽象域对CTL标准算子是完备的当且仅当抽象域对补集和标准前向转换是完备的.     

Intuitionistic fuzzy transformation semigroups

The idea of (faithful) intuitionistic fuzzy transformation semigroup, intuitionistic admissible relation, and intuitionistic (strong) homomorphism are introduced and their basic properties are examined.     

改进的CKF算法及其在BDS/INS中的应用

针对标准容积卡尔曼滤波(CKF)在组合导航系统模型不确定情况下滤波精度下降甚至发散的问题,将奇异值分解(SVD)与CKF算法相结合,并引入强跟踪滤波(STF)理论,提出一种改进的强跟踪SVD-CKF算法.为提高数值计算的稳定性,采用SVD代替标准CKF中的Cholesky分解;引入STF理论框架,通过渐消因子对预测误差协方差阵进行在线修正,在系统模型不确定或系统发生大的突变时,能够提高系统的强鲁棒性.通过仿真结果验证了改进算法的有效性.     

Multi-objective optimization with artificial weed colonies

Invasive Weed Optimization (IWO) was recently proposed as a simple but powerful metaheuristic algorithm for real parameter optimization. IWO draws inspiration from the ecological process of weeds colonization and distribution and is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. This article extends the basic IWO for tackling multi-objective optimization problems that aim at achieving two or more objectives (very often conflicting) simultaneously. The concept of fuzzy dominance has been used to sort the promising candidate solutions at each iteration. The new algorithm has been shown to be statistically significantly better than some state of the art existing evolutionary multi-objective algorithms, namely NSGAIILS, DECMOSA-SQP, MOEP, Clustering MOEA, GDE3, and MOEADGM on a 12-function test-suite (including both unconstrained and constrained problems) from the IEEE CEC (Congress on Evolutionary Computation) 2009 competition and special session on multi-objective optimization algorithms. The following performance metrics were considered: IGD, Spacing, and Minimum Spacing. Our experimental results suggest that IWO holds immense promise to appear as an efficient metaheuristic for multi-objective optimization.     

基于F2上遍历矩阵的Shamir三次传递协议的实现

给出了Shamir三次传递协议的一种新的实现方案.与已有的基于离散对数问题的实现方案不同,它是基于在特定的非交换壹半群(m,·)中,由A和B＝x·A·y求解x和y的难度;为此我们选取有限域F2上的n×n矩阵在F2矩阵乘法下所构成的非交换壹半群作为研究的对象,利用F2上"遍历矩阵"的密码学特性,提出了一个基于F2上遍历矩阵的Shamir三次传递协议的实现方案,并对可能的攻击手段进行了分析.为了增加通讯的安全性,提出了"强壮矩阵"的概念,并对于给定的两个遍历矩阵Q1和Q2,给出了关于Q1,Q2的强壮矩阵的判别标准和寻找算法;利用<Q1>,<Q2>以及关于Q1,Q2的强壮矩阵m,可以构造一个单向(陷门)函数,基于该单向函数,可具体实现Shamir三次传递协议、Diffie-Hellman密钥交换协议以及常规的公钥密码.     

Coefficient regularized regression with non-iid sampling

In this paper, we study a more general kernel regression learning with coefficient regularization. A non-iid setting is considered, where the sequence of probability measures for sampling is not identical but the sequence of marginal distributions for sampling converges exponentially fast in the dual of a Holder space; the sampling z _i , i ≥ 1 are weakly dependent, which satisfy a strongly mixing condition. Satisfactory capacity independently error bounds and learning rates are derived by the techniques of integral operator for this learning algorithm.