在多层神经网络中用Back-Propagation算法进行方位角估测

本文介绍了多层神经网络的基本结构和主要概念,并对训练多层神经网络的Back-Propagation学习算法(即后向传递误差算法,简称后向算法)的原理和实施步骤作了详尽的分析和推导。在多层神经网络中运用这一算法,提出了平面波方位角估测的新方法。计算机模拟结果显示,这一方法是可行     

认知空间上的智能系统FDSS

智能系统ＦＤＳＳ是直接构造出表现模糊逻辑的一套硬件。作为专家系统的核心,可以通过从经验规则而来的分立电子线路模块与计算机接口相缝合,达到使形式化知识及非形式化知识融溶一体,综合运用的目的。     

模糊线性规划的对称曲边岭形模型

用扩张原理,对模糊线性规划中参数为对称等腰岭形的情形建立了相应的模型化公式,并把这类问题转化为非线性规划求解     

广义拟Sugeno模糊积分的确界式等价表示

通过减弱公理化条件给出新拟乘算子, 并按照拟乘运算针对实函数正(负)部建立了广义拟Sugeno模糊积分. 依据演化的确界概念获得了该积分的3种表达形式, 并给出了其等价表示及其证明. 最后, 通过实例验证了该广义拟Sugeno模糊积分3种等价表示的正确性.     

直觉判断矩阵的弱传递性与加型一致性研究

定义了直觉判断矩阵的有向图和有向路概念,证明在一定的条件下直觉判断矩阵的有向图有唯一有向路.利用该结论给出一个简洁的排序方法,并指出这种方法的不足之处.通过反例说明了直觉判断矩阵已有的加型一致性概念与弱传递性概念之间没有必然强弱关系.根据直觉模糊数的得分值和精确度值,利用效用的思想给出直觉判断矩阵加型一致性的新定义,并证明了新定义的加型一致性具有弱传递性.针对一般的直觉判断矩阵, 运用新定义的加型一致性概念以及偏差函数的方法给出了求解直觉判断矩阵的直觉模糊数型权重的模型,并通过算例验证了决策方法的实用性.     

Primal-dual proximal point algorithm for linearly constrained convex programming problems

A primal-dual version of the proximal point algorithm is developed for linearly constrained convex programming problems. The algorithm is an iterative method to find a saddle point of the Lagrangian of the problem. At each iteration of the algorithm, we compute an approximate saddle point of the Lagrangian function augmented by quadratic proximal terms of both primal and dual variables. Specifically, we first minimize the function with respect to the primal variables and then approximately maximize the resulting function of the dual variables. The merit of this approach exists in the fact that the latter function is differentiable and the maximization of this function is subject to no constraints. We discuss convergence properties of the algorithm and report some numerical results for network flow problems with separable quadratic costs.     

Sufficiency and fuzziness in random experiments

In previous papers, the consequences of the “presence of fuzziness” in the experimental information on which statistical inferences are based were discussed. Thus, the intuitive assertion «fuzziness entails a loss of information» was formalized, by comparing the information in the “exact case” with that in the “fuzzy case”. This comparison was carried out through different criteria to compare experiments (in particular, that based on the “pattern” one, Blackwell's sufficiency criterion). Our purpose now is slightly different, in the sense that we try to compare two “fuzzy cases”. More precisely, the question we are interested in is the following: how will different “degrees of fuzziness” in the experimental information affect the sufficiency? In this paper, a study of this question is carried out by constructing an alternative criterion (equivalent to sufficiency under comparability conditions), but whose interpretation is more intuitive in the fuzzy case. The study is first developed for Bernoulli experiments, and the coherence with the axiomatic requirements for measures of fuzziness is also analyzed in such a situation. Then it is generalized to other random experiments and a simple example is examined.     

Urn模型的B网及其应用

Urn模型的离散概率分布被用于构造逼近格式,具有Bernstein多项式的许多特性。本文用B网技术对Urn模型概率分布给出新的解释,指出了Urn模型B网的诸多性质,导出分布函数曲线的导数公式,构造了Urn分布的对偶基及与此相关的拟插值算子。     

一种迭代格式的有限元并行算法*

本文提出了一种求解有限元方程的迭代格式的并行算法.该方法在线性代数方程迭代解法的基础上,引进并行运算步骤;并且运用加权残数方法,通过选择适当的权函数,推导了该并行算法的有限元基本格式.该方法在西安交通大学BLXSI-6400并行计算机上程序实现.计算结果表明它能有效地提高运算速度,减少计算时间,是一种有效的求解大型结构有限元方程的并行算法.     

弹性接触问题参数变分原理的有限元并行算法*

本文基于弹性接触问题的参数变分原理的有限元解法,利用并行计算机的特性和并行处理结构,建立了相应的并行算法.该算法从刚度阵的生成和组集,静凝聚过程,求应力过程等多方面实现了并行化.该算法在西安交通大学ELXSI-6400并行计算机上程序实现,计算结果表明能有效地节省计算时间,是一种分析接触问题的有效的并行算法.