一类带小时滞的非线性快慢系统的渐近解

研究了一类带小时滞的非线性快慢系统的初始值问题,在一定假设条件下,利用奇异摄动理论和校正函数法构造了该问题的形式渐近解,并利用微分不等式理论证明了渐近解的一致有效性.最后进行了算例分析,结果显示时滞能对快慢系统产生重要影响,并表明所述摄动方法是一个行之有效的近似解析方法.从而,可以利用得到的渐近解对系统的动力学行为进行更深层次地分析与研究.     

时滞扰动类Lorenz系统的Hopf分岔

通过非线性动力学理论,对时滞类Lorenz系统在平衡点的稳定性问题和发生Hopf分岔的条件进行了研究.首先计算得到系统的平衡点,然后通过分析系统在平衡点处的相应特征方程根的分布,得到系统在平衡点局部渐近稳定和产生Hopf分岔的时滞临界点.以时滞为分叉参数,研究了时滞系统存在Hopf分岔的条件.最后,利用Matlab程序进行仿真验证所得结论与理论分析一致.本文的结论是对一些已有文献研究成果的推广.     

单时滞类Chen系统Hopf分岔分析

根据非线性动力学理论,以一类新的单时滞Chen系统为分析对象,针对其平衡点的稳定性和Hopf分岔参数等问题进行研究.根据Routh-Hurwitz判据分析了其平衡点的稳定性,通过计算得到单时滞Chen系统特征根的分布,进一步分析得出系统在零平衡点附近是渐进稳定的.结合Hopf分岔理论,运用特征根的分布结果,确定出系统发生Hopf分岔的时滞参数,并给出Hopf分岔条件.通过多组实验仿真验证了理论分析的正确性.     

生成学习系统的混沌与设计

认知学习是一种高度复杂的非线性现象.试图依据生成学习理论和经验学习的思想,建构含有记忆效应的生成学习系统动力学模型,探讨认知学习过程的复杂现象和变化特征,揭示学习系统波动的内生机制和学生认知的混沌规律,并在此基础上提出基于学习混沌的教学系统设计模式,期望能促进学生认知结构的发展.     

基于随机时滞模型的学习成绩波动规律

依据学习机制和教育心理学、认知心理学等有关研究成果,运用动力系统、随机过程等理论,构建描述学习成绩变化的随机时滞模型,并通过模型动力学性态的研究来揭示学习成绩波动特性,预测学习成绩对学习系统中内外因素的反应,分析造成学习成绩波动的原因,探讨相应的教学干预策略.结果表明,学习成绩波动是由4种动力学模式主导;学习成绩对学习系统内外因素具有高度的敏感性.这将从理论上加深对认知涌现机制的认识,并对于拓展和改进教学思维有积极意义.     

一类具有垂直传染与脉冲免疫的SEIR传染病模型的全局分析

考虑了一个具有垂直传染与积分时滞的SEIR传染病动力学模型.分析了该模型在脉冲免疫接种条件下的动力学行为,获得了传染病灭绝的充分条件,进而运用脉冲时滞泛函微分方程理论,获得了含有时滞的系统持久性的充分条件,并且证明了积分时滞与脉冲免疫能对模型的动力学行为产生显著的影响.     

非线性期望的理论、方法及意义

本文是非线性期望理论进展的一个综述,首先给出非线性期望空间的基本定义,并通过非线性期望的表示定理和几个典型的非线性独立同分布(i.i.d.)的例子来说明为什么这个新框架可以广泛地用来分析和计算现实世界(高维)数据背后隐藏的概率和统计分布的不确定性;进而介绍次线性期望空间中两个最重要的统计分布—非线性正态分布和最大分布,以及相应的非线性大数定律和中心极限定理,是新领域的基础性和关键性的突破,其典型的应用就是对于现实的(高维)样本数据的非常简单而深刻的φ-max-mean算法.本文还介绍一个最重要的连续时间随机过程——非线性Brown运动及相关随机分析,包括随机积分、随机微分方程和非线性鞅理论.新的理论框架实质性地推广了Kolmogorov于1933年建立的、以概率测度为核心的概率论公理体系(?,F,P).其关键不同的是,其核心概念是(非线性)期望ê,期望为线性的特殊情形对应着概率论公理体系.正是这种非线性使人们能够对于现实世界中无处不在的概率模型本身的不确定性也能进行定量的分析和计算.从而实质性地放宽了概率统计理论中对于现实世界的随机数据的统计假设要求,本文也因而获得了实际样本数据的非线性分布的φ-max-mean算法,它是一种新的非线性Monté-Carlo算法.     

随机时滞控制系统的均方BIBO稳定性

本文研究了具有时滞和非线性扰动的随机控制系统的均方有界输入-有界输出(BIBO)稳定.首先,探讨了具有离散时滞和非线性扰动的随机系统的均方BIBO稳定性问题,在此基础上,进一步研究带有离散时滞和分布时滞以及非线性扰动的随机系统的均方BIBO稳定性.通过设计合理的控制器,建立合适的Lyapunov泛函,结合Riccati矩阵方程,得到时滞依赖的均方BIBO稳定性条件.     

时滞反馈作用下ENSO充电振子模型的分岔分析

通过数学变换将一类含有时滞反馈机制的ENSO充电振子模型转换成时滞Van der PolDuffing方程,并以此为基础来研究该ENSO系统的零解稳定性、Hopf分岔和极限环等动力学特征.用平均法分析了其零解的稳定性与时滞效应的强度,其和时间都有关系,讨论了时滞负反馈对ENSO振荡的影响并通过简单数值模拟验证理论分析的结果.     

一类TCP系统的局部Hopf分支和周期解的全局存在性

研究了一类一阶非线性时滞微分方程描述的TCP系统的动力学行为.通过分析其相应的特征超越方程,得到了当时滞通过一系列临界值时,在正平衡点处Hopf分支产生.利用中心流形和规范型理论,得到了确定Hopf分支方向和稳定性的具体计算表达式.运用Wu[Trans Amer MathSoc,1998,350(12):4799-4838]的方法,得到了全局Hopf分支存在的条件.     

Recent advances in robust optimization: An overview

This paper provides an overview of developments in robust optimization since 2007. It seeks to give a representative picture of the research topics most explored in recent years, highlight common themes in the investigations of independent research teams and highlight the contributions of rising as well as established researchers both to the theory of robust optimization and its practice. With respect to the theory of robust optimization, this paper reviews recent results on the cases without and with recourse, i.e., the static and dynamic settings, as well as the connection with stochastic optimization and risk theory, the concept of distributionally robust optimization, and findings in robust nonlinear optimization. With respect to the practice of robust optimization, we consider a broad spectrum of applications, in particular inventory and logistics, finance, revenue management, but also queueing networks, machine learning, energy systems and the public good. Key developments in the period from 2007 to present include: (i) an extensive body of work on robust decision-making under uncertainty with uncertain distributions, i.e., “robustifying” stochastic optimization, (ii) a greater connection with decision sciences by linking uncertainty sets to risk theory, (iii) further results on nonlinear optimization and sequential decision-making and (iv) besides more work on established families of examples such as robust inventory and revenue management, the addition to the robust optimization literature of new application areas, especially energy systems and the public good.     

Interpreting supply chain dynamics: A quasi-chaos perspective

Chaotic phenomena, chaos amplification and other interesting nonlinear behaviors have been observed in supply chain systems. Chaos can be defined theoretically if the dynamics under study are produced only by deterministic factors. However, deterministic settings rarely present themselves in reality. In fact, real data are typically unknown. How can the chaos theory and its related methodology be applied in the real world? When the demand is stochastic, the interpretation and distribution of the Lyapunov exponents derived from the effective inventory at different supply chain levels are not similar to those under deterministic demand settings. Are the observed dynamics of the effective inventory random, chaotic, or simply quasi-chaos? In this study, we investigate a situation whereby the chaos analysis is applied to a time series as if its underlying structure, deterministic or stochastic, is unknown. The result shows clear distinction in chaos characterization between the two categories of demand process, deterministic vs. stochastic. It also highlights the complexity of the interplay between stochastic demand processes and nonlinear dynamics. Therefore, caution should be exercised in interpreting system dynamics when applying chaos analysis to a system of unknown underlying structure. By understanding this delicate interplay, decision makers have the better chance to tackle the problem correctly or more effectively at the demand end or the supply end.     

Stabilization of Passive Nonlinear Stochastic Differential Systems by Bounded Feedback

Abstract

The purpose of this paper is to give a systematic method for global asymptotic stabilization in probability of nonlinear control stochastic differential systems the unforced dynamics of which are Lyapunov stable in probability. The approach developed in this paper is based on the concept of passivity for nonaffine stochastic differential systems together with the theory of Lyapunov stability in probability for stochastic differential equations. In particular, we prove that, as in the case of affine in the control stochastic differential systems, a nonlinear stochastic differential system is asymptotically stabilizable in probability provided its unforced dynamics are Lyapunov stable in probability and some rank conditions involving the affine part of the system coefficients are satisfied. Furthermore, for such systems, we show how a stabilizing smooth state feedback law can be designed explicitly. As an application of our analysis, we construct a dynamic state feedback compensator for a class of nonaffine stochastic differential systems.     

Dimensionality reduction and greedy learning of convoluted stochastic dynamics

Complex natural systems may present interaction dynamics among random variables whose stochastic laws are in part or completely unknown. Statistical inference techniques applied to study such complex systems often require building suitable models that approximately describe the latent stochastic dynamics. When the observability of the variables of interest is limited by the convolution of such dynamics and noise, deconvolution techniques are needed either to estimate statistical characteristics or to decompose mixed signals. A good application field is offered by speculative financial market and their volatility stochastic dynamics. Typically, return generating stochastic processes show nonlinear, multiscale and non-stationary dynamics, especially when observed at very high frequencies. We explore the performance of computational techniques that combine the nonlinear approximation power of wavelets and associated structures with the ability of greedy learning algorithms to recover latent volatility structure by iteratively reducing the signal search space dimensionality across the most informative scales.     

A behavioral stock market model

Stock exchanges are modeled as nonlinear closed-loop systems where the plant dynamics is defined by known stock market regulations and the actions of agents are based on their beliefs and behavior. The decision of the agents may contain a random element, thus we get a nonlinear stochastic feedback system. The market is in equilibrium when the actions of the agents reinforce their beliefs on the price dynamics. Assuming that linear predictors are used for prediction of the price process, a stochastic approximation procedure for finding market equilibrium is described. The proposed procedure is analyzed using the theory of Benveniste et al. (Adaptive algorithms and stochastic approximations. Springer, Berlin, 1990). A simulation result is also presented.     

Synchronization of networks with time-varying couplings

In this paper, we present a review of our recent works on complete synchro-nization analyses of networks of the coupled dynamical systems with time-varying cou-plings. The main approach is composed of algebraic graph theory and dynamic system method. More precisely, the Hajnal diameter of matrix sequence plays a key role in in-vestigating synchronization dynamics and the joint graph across time periods possessing spanning tree is a doorsill for time-varying topologies to reach synchronization. These techniques with proper modification count for diverse models of networks of the cou-pled systems, including discrete-time and continuous-time models, linear and nonlinear models, deterministic and stochastic time-variations. Alternatively, transverse stability analysis of general time-varying dynamic systems can be employed for synchronization study as a special case and proved to be equivalent to Hajnal diameter.     

Subregular recourse in nonlinear multistage stochastic optimization

We consider nonlinear multistage stochastic optimization problems in the spaces of integrable functions. We allow for nonlinear dynamics and general objective functionals, including dynamic risk measures. We study causal operators describing the dynamics of the system and derive the Clarke subdifferential for a penalty function involving such operators. Then we introduce the concept of subregular recourse in nonlinear multistage stochastic optimization and establish subregularity of the resulting systems in two formulations: with built-in nonanticipativity and with explicit nonanticipativity constraints. Finally, we derive optimality conditions for both formulations and study their relations.

     

A simultaneous perturbation weak derivative estimator for stochastic neural networks

Computational Management Science - In this paper we study gradient estimation for a network of nonlinear stochastic units known as the Little model. Many machine learning systems can be described...     

Engineering design and the development of knowledge for teaching among preservice science teachers

The goal of this article is to inform professional understanding regarding preservice science teachers’ knowledge of engineering and the engineering design process. Originating as a conceptual study of the appropriateness of “knowledge as design” as a framework for conducting science teacher education to support learning related to engineering design, the findings are informed by an ongoing research project. Perkins’s theory encapsulates knowledge as design within four complementary components of the nature of design. When using the structure of Perkins’s theory as a framework for analysis of data gathered from preservice teachers conducting engineering activities within an instructional methods course for secondary science, a concurrence between teacher knowledge development and the theory emerged. Initially, the individuals, who were participants in the research, were unfamiliar with engineering as a component of science teaching and expressed a lack of knowledge of engineering. The emergence of connections between Perkins’s theory of knowledge as design and knowledge development for teaching were found when examining preservice teachers’ development of creative and systematic thinking skills within the context of engineering design activities as well as examination of their knowledge of the application of science to problem‐solving situations.     

ESTIMATING A STOCHASTIC MODEL OF POPULATION DYNAMICS WITH AN APPLICATION TO KANGAROOS

In this study, we derive stochastic models of population dynamics and devise a new method of estimating the models. The models allow growth and harvest to be nonlinear functions of stochastic processes and the error terms to be nonlinear and heteroskedastic. Ordinary least-squares estimates would be biased and inefficient and generalized least-squares estimates cannot be calculated. Therefore, we implement nonlinear maximum likelihood methods to find unbiased and efficient estimates of parameters. The method is applied to the population dynamics of kangaroos in South Australia. Aerial survey data of kangaroo numbers are combined with harvest, effort and rainfall data to estimate the growth and harvest functions and the variances of the stochastic processes which drive the system. Results suggest that growth and harvest should be modeled as functions of stochastic processes and that observations on kangaroo numbers are critical for estimating population dynamics. The results also indicate that the estimation method works well and is a viable alternative to ARIMA and GARCH models, particularly for small data sets.