一类不动子集的泛系研究

本文继续有关不动子集的泛系研究.在泛系框架下得到了Ⅰ型不动子集的存在准则;给出了自反关系类及等价关系类与不动子集的关系定理;引进了二元关系的不动点概念并对一类泛权网络建立了几个不动点存在定理.     

自然资源的运筹分析及其泛权场网模型*

本文提出了竞分三故原则,很自然地把自然资源、能源、人口和环境等问题统一到一个共同的模式之中,为这些问题的综合研究提供了一个基本框架,使得自然资源运筹分析的思路更加清晰,各部分之间的关系更加明了.在此基础上,本文提出了多个场网之间运筹分析的数学模型,为定量解决全球性的综合问题奠定了基础.我们特别讨论了自然资源-竞分者两个场网之间的生克关系和运筹分析的数学模型,为定量解决资源-人口,资源-经济等问题提供了数学工具.     

复合挤压力的上限解

本文根据实验和滑移线场拟定复合挤压时的速度不连续刚性三角形,由此得出复合挤压时凸模单位压力的最小上限解的解析式,将此式解值和实测值进行比较,表明上限值可供实际使用.     

圆柱正交异性复合厚壁圆筒的弹-塑性分析

本文导出了不可压缩和可压缩材料,平面应变问题的Tsai-Hill屈服准则形式;研究了在均匀径向压力作用下圆柱正交异性复合厚壁圆筒的弹-塑性应力场和位移场.求得了弹性屈服压力、极限载荷和安定载荷的公式.     

残余界面应力对粒子填充热弹性纳米复合材料有效热膨胀系数的影响

根据黄筑平等人提出的基于“3个构形”的表/界面能理论,研究了热弹性纳米复合材料的有效性质,重点讨论了残余界面应力对纳米尺度夹杂填充的热弹性复合材料有效热膨胀系数的影响．首先,给出了由第一类Piola-Kirchhoff界面应力表示的热弹性界面本构关系和Lagrange描述下的Young-Laplace方程;其次,采用Hashin复合球作为代表性体积单元,推导了在参考构形下复合球内部由残余界面应力诱导的残余弹性场,并进一步计算了从参考构形到当前构形的变形场;最后,基于以上计算得到了热弹性复合材料有效体积模量和有效热膨胀系数的解析表达式．研究表明,残余表/界面应力对复合材料的热膨胀系数有重要影响．     

复合挤压时的杆部表面裂纹分析

本文依据实验结果拟定了轴对称杯杆型复合挤压出现杆部表面裂纹时的金属流动速度场,以此为基础,并藉助于上限原理和最小能量原理,获得了此类复合挤压时杆部表面裂纹形成的必要条件。同时,研究了此类复合挤压杆部变形开裂区与正挤,反挤部分变形程度(εf,εb)组合、坯料相对余厚(T/R_0)、摩擦因数m值以及模具工作带相对长度(l_f/R_0,l_b/R_0)的关系.从而可以估测低塑性材料在进行此类复合挤压时杆部是否形成表面裂纹.LY12和LC4材料的试验结果与本文的分析结果具有很好的一致性.     

排球的发球问题

实际问题：在排球比赛中,地运动员站在后场发球．已知：排球的直径为0．21米（规定在20．69cm至21．327cm之间）,排球场总长为18米,网高2．5米,女子网高为2．24米;为计算方便不妨取2．5米）．运动员采用跳发球的方式以水平方向发球,为了使发出的球落在界内．     

受热弹性复合球体空化问题的分析

研究了由两种弹性固体材料组成的复合球体,在均匀变温场作用下的空化问题．采用了几何大变形的有限对数应变度量和Hooke弹性固体材料的本构关系,建立了问题的非线性数学模型．求出了复合球体大变形热弹性膨胀的参数形式的解析解．给出了空穴萌生时临界温度随几何参数和材料参数的变化曲线,以及空穴增长的分岔曲线．算例的数值结果指出:超过临界温度后空穴半径将迅速增大,并且空穴萌生时环向应力将成为无限大,这意味着如果内部球体是弹塑性材料,则会在空穴表面附近产生塑性变形而造成材料的局部损伤．另外,当内部球体材料的弹性接近于不可压时,复合球体可以在较低的变温下空化．     

数学物理中的总体和局部场GL方法

为了求解物理化学生物材料和金融中的微分方程,提出了一种总体(Global)和局部(Local)场方法.微分方程的求解区域可以是有限域,无限域,或具曲面边界的部分无限域.其无限域包括有限有界不均匀介质区域.其不均匀介质区域被分划为若干子区域之和.在这含非均匀介质的无限区域,将微分方程的解显式地表示为在若干非均匀介质子区域上和局部子曲面的积分的递归和.把正反算的非线性关系递归地显式化.在无限均匀区域,微分方程的解析解被称为初始总体场.微分方程解的总体场相继地被各个非均匀介质子区域的局部散射场所修正.这种修正过程是一个子域接着另个子域逐步相继地进行的.一旦所有非均匀介质子区域被散射扫描和有限步更新过程全部完成后,微分方程的解就获得了.称其为总体和局部场的方法,简称为GL方法.GL方法完全地不同于有限元及有限差方法,GL方法直接地逐子域地组装逆矩阵而获得解.GL方法无需求解大型矩阵方程,它克服了有限元大型矩阵解的困难.用有限元及有限差方法求解无限域上的微分方程时,人为边界及其上的吸收边界条件是必需的和困难的,人为边界上的吸收边界条件的不精确的反射会降低解的精确度和毁坏反算过程.GL方法又克服了有限元和有限差方法的人为边界的困难.GL方法既不需要任何人为边界又不需要任何吸收边界条件就可以子域接子域逐步精确地求解无限域上的微分方程.有限元和有限差方法都仅仅是数值的方法,GL方法将解析解和数值方法相容地结合起来.提出和证明了三角的格林函数积分方程公式.证明了当子域的直经趋于零时,波动方程的GL方法的数值解收敛于精确解.GL方法解波动方程的误差估计也获得了.求解椭圆型,抛物线型,双曲线型方程的GL模拟计算结果显示出我们的GL方法具有准确,快速,稳定的许多优点.GL方法可以是有网,无网和半网算法.GL方法可广泛应用在三维电磁场,三维弹塑性力学场,地震波场,声波场,流场,量子场等方面.上述三维电磁场等应用领域的GL方法的软件已经由作者研制和发展了。     

接触表面中多场耦合方程的边界层特征解

介绍由约束场和受重力影响的对流扰动耦合而成的衰减平衡向量场动力学方程的渐近求解.为分析实验室内微观与自然界中宏观现象的正则和奇异扰动问题.运用复合尺度方法进行Fourier调和分析、尺度变化,并引进新的参数,将一个复杂的三维约束耦合动力学方程降维投影并转化成复空间里一维的边界层问题.通过渐近摄动分析,给出多场耦合中扰动问题的特征函数边界层解法,在例2中对流场扰动问题分析,得出从指数振荡解过渡到代数解的转点.进一步分析计算非线性特征值问题并做了渐近摄动分析,最后给出多场耦合中扰动问题的特征值边界层解法.最后,特征关系式的各参数表明其在接触表面中对动力衰变的关键影响.     

Exponential synchronization of stochastic fuzzy cellular neural networks with time delay in the leakage term and reaction-diffusion

Separate studies have been published on the stability of fuzzy cellular neural networks with time delay in the leakage term and synchronization issue of coupled chaotic neural networks with stochastic perturbation and reaction-diffusion effects. However, there have not been studies that integrate the two fields. Motivated by the achievements from both fields, this paper considers the exponential synchronization problem of coupled chaotic fuzzy cellular neural networks with stochastic noise perturbation, time delay in the leakage term and reaction-diffusion effects using linear feedback control. Lyapunov stability theory combining with stochastic analysis approaches are employed to derive sufficient criteria ensuring the coupled chaotic fuzzy neural networks to be exponentially synchronized. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.     

Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings

Social action is situated in fields that are simultaneously composed of interpersonal ties and relations among organizations, which are both usefully characterized as social networks. We introduce a novel approach to distinguishing different network macro-structures in terms of cohesive subsets and their overlaps. We develop a vocabulary that relates different forms of network cohesion to field properties as opposed to organizational constraints on ties and structures. We illustrate differences in probabilistic attachment processes in network evolution that link on the one hand to organizational constraints versus field properties and to cohesive network topologies on the other. This allows us to identify a set of important new micro-macro linkages between local behavior in networks and global network properties. The analytic strategy thus puts in place a methodology for Predictive Social Cohesion theory to be developed and tested in the context of informal and formal organizations and organizational fields. We also show how organizations and fields combine at different scales of cohesive depth and cohesive breadth. Operational measures and results are illustrated for three organizational examples, and analysis of these cases suggests that different structures of cohesive subsets and overlaps may be predictive in organizational contexts and similarly for the larger fields in which they are embedded. Useful predictions may also be based on feedback from level of cohesion in the larger field back to organizations, conditioned on the level of multiconnectivity to the field.     

Fuzzy Entropy： Axiomatic Definition and Neural Networks Model

The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.     

Pinning adaptive synchronization of a class of uncertain complex dynamical networks with multi-link against network deterioration

For the reason that the uncertain complex dynamic network with multi-link is quite close to various practical networks, there is superiority in the fields of research and application. In this paper, we focus upon pinning adaptive synchronization for uncertain complex dynamic networks with multi-link against network deterioration. The pinning approach can be applied to adapt uncertain coupling factors of deteriorated networks which can compensate effects of uncertainty. Several new synchronization criterions for networks with multi-link are derived, which ensure the synchronized states to be local or global stable with uncertainty and deterioration. Results of simulation are shown to demonstrate the feasibility and usefulness of our method.     

Boolean autoencoders and hypercube clustering complexity

We introduce and study the properties of Boolean autoencoder circuits. In particular, we show that the Boolean autoencoder circuit problem is equivalent to a clustering problem on the hypercube. We show that clustering m binary vectors on the n-dimensional hypercube into k clusters is NP-hard, as soon as the number of clusters scales like ${m^\epsilon (\epsilon >0 )}$ , and thus the general Boolean autoencoder problem is also NP-hard. We prove that the linear Boolean autoencoder circuit problem is also NP-hard, and so are several related problems such as: subspace identification over finite fields, linear regression over finite fields, even/odd set intersections, and parity circuits. The emerging picture is that autoencoder optimization is NP-hard in the general case, with a few notable exceptions including the linear cases over infinite fields or the Boolean case with fixed size hidden layer. However learning can be tackled by approximate algorithms, including alternate optimization, suggesting a new class of learning algorithms for deep networks, including deep networks of threshold gates or artificial neurons.     

Quantum systems can realize content-addressable associative memory

It is presented how associative information processing can be implemented in quantum fields. Similarly to networks of coupled oscillators, quantum associative networks exploit correlations and phase-differences among “interfering eigen-wave-functions” for memorization and recognition of patterns.     

Impact of Correlated Activity and STDP on Network Structure

Synaptic strengths between neurons are plastic and modified by spontaneous activity and information from the outside. There is increasing interest in the impact of correlated neuron activity and learning rules on global network structure. Here the networks of exponential integrate-and-fire neurons with spike timing-dependent plasticity (STDP) learning rules are considered, by providing the theoretical approximation of spiking cross-covariance between connected neurons and the theory for the evolution of synaptic weights. Background input mean and variance highly affect the spiking covariance, even for the fixed baseline firing rate and connection. Through analyzing the effects of covariance and STDP on vector fields for pairwise correlated neurons under fixed baseline firing rate, we show that the connections from a neuron with lower input mean to that with higher one will strengthen for balanced Hebbian STDP. However, this situation is reversed for Anti-Hebbian cases. Moreover, for potentiation dominated STDP, the synaptic weights for the networks of neurons with lower input mean are more likely to be enhanced. In addition, these properties found from coupled neurons also hold for large recurrent networks in both theories and simulations. This study provides a self-consistent theoretical method for understanding how correlated spiking activity and STDP shape the network structure and an approach for predicting structures of large networks through the analysis of simple neural circuits.     

On the bicriterion – minimal cost/minimal label – spanning tree problem

We address a bicriterion spanning tree problem relevant in some application fields such as telecommunication networks or transportation networks. Each edge is assigned with a cost value and a label (such as a color). The first criterion intends to minimize the total cost of the spanning tree (the summation of its edge costs), while the second intends to get the solution with a minimal number of different labels. Since these criteria, in general, are conflicting criteria we developed an algorithm to generate the set of non-dominated spanning trees. Computational experiments are presented and results discussed.     

具有跳跃的随机神经网络的均方稳定性

研究了一类新的具有脉冲跳跃的Hopfield神经网络系统模型,其中脉冲时刻的跳跃是由一般的随机序列所引起,通过运用Lyapunov函数方法,获取了一些新的均方稳定性结果.由于脉冲的跳跃使得不稳定的神经网络变成稳定,因而所得的结果也可以运用到其他相关领域.     

Importance measures in reliability and mathematical programming

The importance measures have been a sensitivity analysis for a probabilistic system and are applied in diverse fields along with other design tools. This paper provides a comprehensive view on modeling the importance measures to solve the reliability problems such as component assignment problems, redundancy allocation, system upgrading, and fault diagnosis and maintenance. It also investigates importance measures in broad applications such as networks, mathematical programming, sensitivity and uncertainty analysis, and probabilistic risk analysis and probabilistic safety assessment. The importance-measure based methods are among the most practical decision tools.