再分配伪逆算法分配效率研究

多操纵面先进战斗机在进行舵面分配设计时,分配效率是衡量分配算法优劣的一个重要指标.再分配伪逆算法(RPI)的分配效率取决于伪逆阵的选择,其可达转矩集是一个复杂的非凸多面体.本文利用"微元"的求解思路,对二维及三维RPI算法的分配效率进行了研究,给出一种近似求解RPI可达转矩集的方法.将RPI分配效率作为适应度函数,通过遗传算法来选择具有最优分配效率的广义逆阵,从而提高RPI算法的分配效率.以某飞机的数据进行基于RPI的分配器设计,结果显示此方法显著提高了算法的分配效率.     

结构包含密集模态时主动振动控制中作动器/传感器的优化配置

分析了已有的作动器/传感器配置方法的不足,针对包含密集模态的挠性结构,分析其可控/可观度的特殊性,提出了针对密频系统的作动器/传感器优化配置准则.通过可控性Gram阵的分块解析形式,避免了求解高阶Lyapunov矩阵方程,并利用了轻阻尼疏频系统自然平衡的条件,降低了求解可控度,即可控性矩阵奇异值的计算量.在此基础上用遗传算法寻优,得到元件的优化配置方式.最后通过算例与另外两种基于可控度的优化配置方法对比,证明依本文方法配置密频系统的作动器/传感器,振动控制效果最优.     

基于遗传算法的模拟退火罚函数方法求解地下水管理模型

提出一种新的基于遗传算法的模拟退火罚函数方法求解地下水管理模型。遗传算法及建立在自然遗传学和自然选择机理上的全局随机搜索和进化的过程。与传统的基于梯度寻优方法相比,遗传算法寻优不须优化问题连续可导,同时通过模拟退火罚函数方法来处理约束条件,可以保证算法逐渐收敛于可行的最优解,克服一般遗传算法中罚因子选取的困难。     

自适应混沌克隆进化规划算法

结合混沌和抗体克隆选择学说，提出一种新的人工免疫系统算法——自适应混沌克隆进化规划算法．新算法基于Logistic混沌序列；利用个体质量、进化代数和个体分布情况构造混沌变异算子；通过Logistic混沌序列自适应调整变异尺度，理论分析和仿真实验表明，与标准的遗传算法和采用随机变异的克隆选择算法相比，该算法收敛速度快，求解精度高，稳定性好，并有效抑制了早熟现象。     

悬索桥几何非线性及主缆受力分析

利用离散、节点平衡求解、系统平衡协调迭代的方法求得具有确定长度的缆索在荷载作用下的曲线形式，详细分析了缆索在荷载作用下的挠度及变形。然后将悬索桥分为两大子结构，即主缆柔性结构，及加劲梁、塔柱等劲性结构。在两大子结构间的耦合点建立平衡方程，进行直接迭代求解。文中并对目前较为常用的结构几何非线性求解法进行了深入的探讨。通过算例证明本文方法不仅计算简单，迭代自由度少，而且计算结果完全可以与精确解媲美，完全克服了非线性方程求解中解的飘移现象。     

城市公交车路线选择的遗传算法

针对公交车路线的特点，设计了基于变长度染色体编码的遗传算法，求解固定始发站与终到站间的公交车路线优化选择问题，并采用了适合该编码方式的遗传算子。     

基于总量控制的流域允许取水量与允许排污量统一分配模型

为落实初始水权分配方案,实现允许取水量与允许排污量的统一分配,建立了以初始水权为允许取水量总量控制目标、水量分配仿真模型与纳污能力计算模型相耦合的流域允许取水量与允许排污量统一分配模型;针对模型的复杂性,提出了一套显著提高分配方案可操作性与求解效率的水库群优化调控新技术,该技术综合了系统仿真、轮库迭代与智能计算方法.以北江流域为背景,阐释了模型的建立、求解和应用过程;绘制了大型水库优化调度图,获得了流域水资源系统的长期运行策略,给出了任意年型允许取水量与允许排污量分配方案以及跨行政区断面流量过程,分析了模型的有效性和分配方案的合理性.     

一种基于重复博弈的频谱分配策略

针对动态频谱接入，提出了一种基于重复博弈的频谱共享分配策略。通过基于惩罚的防欺骗策略，参与用户将约束自己的行为以合作方式共享频谱、实现频谱分配，保证合作竞争的诚实性。仿真结果显示，该合作规则方法能够很好地降低用户间的干扰，防欺骗方式也能很好地约束参与人保持合作。     

混合量子遗传算法在软硬件协同综合中的应用研究

针对多处理器嵌入式系统,结合拟 Newton算法,提出了混合量子遗传算法(MQGA)在系统设计中的解决方案,并引入了模拟退火技术.实验结果表明,MQGA能有效解决软硬件划分问题,提高了求解质量和算法的收敛速度,降低了计算代价,保证了算法的自适应性和全局最优性     

基于任务的多Agent系统Rough集模型

本文在Rough集框架内建立基于任务的多Agent系统（MAS）形式模型，讨论了任务求解的条件和任务的协同度。在求解基于任务极小MAS的基础上，对Agent进行了基本分类，为Agent的角色划定和责任分配提供依据，同时研究了成员Agent的重要度。     

Stock market volatility and the forecasting performance of stock index futures

This study attempts to apply the general equilibrium model of stock index futures with both stochastic market volatility and stochastic interest rates to the TAIFEX and the SGX Taiwan stock index futures data, and compares the predictive power of the cost of carry and the general equilibrium models. This study also represents the first attempt to investigate which of the five volatility estimators can enhance the forecasting performance of the general equilibrium model. Additionally, the impact of the up‐tick rule and other various explanatory factors on mispricing is also tested using a regression framework. Overall, the general equilibrium model outperforms the cost of carry model in forecasting prices of the TAIFEX and the SGX futures. This finding indicates that in the higher volatility of the Taiwan stock market incorporating stochastic market volatility into the pricing model helps in predicting the prices of these two futures. Furthermore, the comparison results of different volatility estimators support the conclusion that the power EWMA and the GARCH(1,1) estimators can enhance the forecasting performance of the general equilibrium model compared to the other estimators. Additionally, the relaxation of the up‐tick rule helps reduce the degree of mispricing. Copyright © 2008 John Wiley & Sons, Ltd.     

基于节点状态的多接口多信道WMN信道分配算法

多接口多信道无线Mesh两络信道分配的目的在于提升网络容量以及保持网络连通的同时,降低干扰,增强信道负载平衡.由此,提出一种基于节点状态的信道分配方案.该方案是一种基于优先级的多项式时间贪婪混合式算法.NS2仿真结果表明,该算法不仅可以较好地解决信道负载平衡,提升网络容量,而且即使在节点接口的数量少于有效信道数量时,也能使信道得到有效的分配,保证网络的连接通畅.     

基于复杂网络挖掘的用户行为感知机制

用户行为感知是进行网络管理、安全检测以及应用趋势分析的基础.针对基于流量统计特征检测方法具有计算复杂度高和"概念漂移"的缺陷,提出了一种基于用户复杂网络图的用户行为感知机制算法(UBP-CN).算法将用户标识{IP,Port}和用户交互分别抽象为一个点和一条边,构建了用户复杂网络图;应用社团挖掘算法将复杂网络图划分为互不相交的行为子簇,使得用户之间的通信抽象为一种"社会团体";通过定义基于相对熵的"用户行为模式"(UBM),表征了各个子簇背后表现出的行为特性,并使用"UBM+Port"对各个子簇进行标签映射,实现了用户行为的有效感知.仿真结果表明:在不牺牲用户行为分类准确率的前提下,算法不仅能克服"概念漂移"问题,还能有效降低算法的计算复杂度.     

Dynamic harmonic regression

This paper describes in detail a flexible approach to nonstationary time series analysis based on a Dynamic Harmonic Regression (DHR) model of the Unobserved Components (UC) type, formulated within a stochastic state space setting. The model is particularly useful for adaptive seasonal adjustment, signal extraction and interpolation over gaps, as well as forecasting or backcasting. The Kalman Filter and Fixed Interval Smoothing algorithms are exploited for estimating the various components, with the Noise Variance Ratio and other hyperparameters in the stochastic state space model estimated by a novel optimization method in the frequency domain. Unlike other approaches of this general type, which normally exploit Maximum Likelihood methods, this optimization procedure is based on a cost function defined in terms of the difference between the logarithmic pseudo‐spectrum of the DHR model and the logarithmic autoregressive spectrum of the time series. The cost function not only seems to yield improved convergence characteristics when compared with the alternative ML cost function, but it also has much reduced numerical requirements. Copyright © 1999 John Wiley & Sons, Ltd.     

Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union

In this paper we lay out a two‐region dynamic stochastic general equilibrium (DSGE) model of an open economy within the European Monetary Union. The model, which is built in the New Keynesian tradition, contains real and nominal rigidities such as habit formation in consumption, price and wage stickiness as well as rich stochastic structure. The framework also incorporates the theory of unemployment, small open economy aspects and a nominal interest rate that is set exogenously by the area‐wide monetary authority. As an illustration, the model is estimated on Luxembourgish data. We evaluate the properties of the estimated model and assess its forecasting performance relative to reduced‐form model such as vector autoregression (VAR). In addition, we study the empirical validity of the DSGE model restrictions by applying a DSGE‐VAR approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive prediction for stochastic processes with markov jump parameters

This paper is concerned with the adaptive prediction for stochastic processes with abruptly changing parameters modelled as a finite-state Markov chain. The Markov transition matrix is assumed to be known. For the coloured noise disturbance case, it is shown that the optimal prediction algorithm requires a bank of elemental predictors running in parallel with its number growing exponentially with time. If the noise disturbance is white, it is found that the number of the elemental predictors required increases exponentially with the prediction ahead step instead of time. A suboptimal predictor is proposed with substantial reduced storage and computational requirements. Simulation examples show the good performance of the proposed algorithms.     

Information content of DSGE forecasts

This paper examines the question whether information is contained in forecasts from dynamic stochastic general equilibrium (DSGE) models beyond that contained in lagged values, which are extensively used in the models. Four sets of forecasts are examined. The results are encouraging for DSGE forecasts of real GDP. The results suggest that there is information in the DSGE forecasts not contained in forecasts based only on lagged values, and that there is no information in the lagged‐value forecasts not contained in the DSGE forecasts. The opposite is true for forecasts of the GDP deflator.     

Forecasting commodity prices: GARCH,jumps, and mean reversion

In examining stochastic models for commodity prices, central questions often revolve around time‐varying trend, stochastic convenience yield and volatility, and mean reversion. This paper seeks to assess and compare alternative approaches to modelling these effects, with focus on forecast performance. Three specifications are considered: (i) random‐walk models with GARCH and normal or Student‐t innovations; (ii) Poisson‐based jump‐diffusion models with GARCH and normal or Student‐t innovations; and (iii) mean‐reverting models that allow for uncertainty in equilibrium price. Our empirical application makes use of aluminium spot and futures price series at daily and weekly frequencies. Results show: (i) models with stochastic convenience yield outperform all other competing models, and for all forecast horizons; (ii) the use of futures prices does not always yield lower forecast error values compared to the use of spot prices; and (iii) within the class of (G)ARCH random‐walk models, no model uniformly dominates the other. Copyright © 2008 John Wiley & Sons, Ltd.     

有色噪声下随机时滞传染病模型的稳定性

本文运用O-U过程刻画环境变化性，在Edoardo Beretta基础上构造了有色噪声影响下的随机时滞的传染病模型。运用一般Lyapunonv方法研究了有色噪声对该系统的影响并得到系统正平衡点保持稳定的充分条件。最后通过对比发现随机扰动对系统稳定性影响仅仅与其随机过程的方差有关。     

Multivariate Forecasting with BVARs and DSGE Models

In this paper I assess the ability of Bayesian vector autoregressions (BVARs) and dynamic stochastic general equilibrium (DSGE) models of different size to forecast comovements of major macroeconomic series in the euro area. Both approaches are compared to unrestricted VARs in terms of multivariate point and density forecast accuracy measures as well as event probabilities. The evidence suggests that BVARs and DSGE models produce accurate multivariate forecasts even for larger datasets. I also detect that BVARs are well calibrated for most events, while DSGE models are poorly calibrated for some. In sum, I conclude that both are useful tools to achieve parameter dimension reduction. Copyright © 2016 John Wiley & Sons, Ltd.