随机种群模型数值解的均方散逸性

讨论了随机种群模型数值解的均方散逸性,基于步长受限制和无限制的两种条件,利用补偿的和无补偿的数值方法研究了随机种群模型数值解的均方散逸性.从而得出补偿的数值算法更适合解决随机种群模型数值解的均方散逸性问题.     

基于Shifted Legendre多项式非线性年龄结构种群模型的数值解

基于Shifted Legendre多项式研究非线性年龄结构种群模型的数值解问题.定义了在区间[0,A]×[0,T]上函数的Shifted Legendre逼近多项式,通过Shifted Legendre算子矩阵结合Tau方法,把求解非线性年龄结构种群模型的数值解问题转化成非线性代数方程的求解问题.数值算例的结果显示该算法有效.     

温室草莓—灰霉病病菌模型的模糊控制

针对温室草莓感染灰霉病后不能自然治愈的特点,提出了温室草莓—灰霉病病菌模型.在模型分析基础上,建立控制系统模型.针对模型,结合灰霉病病菌种群对草莓种群的危害程度,建立相应的T-S模糊模型.根据模糊控制方法设计控制器,使灰霉病病菌种群密度保持在不影响草莓种群正常生长的水平.最后,利用数值仿真验证了结论的可行性和有效性.     

一类具有时滞的随机SIS传染病模型

本文研究一类具有时滞的随机SIS传染病模型,并定性分析种群灭绝和持久的充分条件.获得了阈值R_0,当R_01时,种群灭绝.当R_01时,种群持久.并通过了数值模拟验证了上述理论结果.     

基于切比雪夫小波基与年龄相关种群模型的数值解

基于切比雪夫小波基给出与年龄相关种群模型的数值解.利用切比雪夫小波基的性质使得所求偏微分方程转化为矩阵方程,从而简化了数值解的求解过程.最后通过数值例子验证其理论结果.     

进化规划动态寻优机理分析

为了从整体上揭示进化规划 ( EP)算法的内在运行机制 ,探寻算法的动态寻优机理 ,本文从种群角度出发 ,建立了一种种群动态寻优微分方程模型 ,并对该模型进行了理论分析 .数值模拟结果说明了所建模型的正确性 ,并得出了一些有意义的结论     

捕食者和食饵均带有扩散的随机捕食-食饵模型动力学分析

考虑了斑块环境下捕食者种群和食饵种群分别在n个斑块扩散的随机捕食 食饵模型.利用Lyapunov函数法证明了对任意给定的初始值,随机系统全局正解的存在唯一性,并对其进行了有界性分析.此外给出了食饵种群及整个系统灭绝的充分条件.最后通过数值模拟验证了所得理论的正确性.     

一类基于集合种群网络的传染病模型研究

研究了一类基于集合种群网络的传染病模型.针对在疾病传播过程中,随着染病者数量的增加,被感染的人数会达到饱和,研究了带有饱和发生率的传染病模型,建立了不同集合种群之间扩散模式,并分析了模型动力学的性态,给出了无病平衡点及其稳定性和正平衡点的存在性.最后用数值模拟验证了理论结果的正确性.     

具有阶段结构和恐惧效应的传染病模型的稳定性与Hopf分支

本文考虑具有阶段结构和恐惧效应的一类种群的传染病模型的稳定性与Hopf分支.首先,分析在一定条件下种群数量的长时间性态;接着利用线性稳定性理论讨论平衡点的局部稳定性,在正平衡点稳定的情况下讨论恐惧程度对易感者幼年种群、易感者成年种群以及染病者成年种群的数量的影响;最后利用分支理论给出Hopf分支存在的条件,并通过数值模拟验证结论的可行性.     

具有环境污染的随机Lotka-Volterra模型周期解的存在性与种群的灭绝性

种群系统在受到环境噪声干扰的同时,还会受到环境污染的影响.讨论了具有环境污染的非自治随机Lotka-Volterra模型.通过构造Lyapunov函数证明了系统正周期解的存在性.运用Ito公式和鞅的强大数定律给出了种群灭绝的充分条件.通过数值例子验证了所得到的结论.     

论大学数学实验课的教学设计

数学既是演绎科学也是实验科学,大学数学实验体现了数学的教育价值.大学数学实验课的教学设计范式包括:基础实验式和综合实验式,基础实验式包括验证性实验式和计算性实验式,综合实验式包括探索性实验式(探究、猜想、模拟)和应用性实验式.针对不同的实验内容,采用不同的教学设计范式来提高大学数学实验课的教学效率.     

Neural net versus classical models for the detection and localization of leaks in pipelines

Four models of a pipeline are compared in the paper: a nonlinear distributed-parameter model, a linear distributed-parameter model, a simplified lumped-parameter model and an extended neural-net-based model. The transcendental transfer function of the linearized model is obtained by a Laplace transformation and corresponding initial and boundary conditions. The lumped-parameter model is obtained by a Taylor series extension of the transencdental transfer function. Based on the experience of linear models the structure of the neural net model, as an addendum to the nonlinear distributed-parameter model, is obtained. All four models are tested on a real pipeline data with an artificially generated leak.     

A novel grey forecasting model and its optimization

Although the grey forecasting model has been successfully employed in many fields and demonstrated promising results, its prediction results may be inaccurate sometimes. For the purposes of enhancing the predictive performance of grey forecasting model and enlarging its suitable ranges, this paper puts forward a novel grey forecasting model termed NGM model and its optimized model, develops a calculative formula for solving the parameters of the novel NGM model through the least squares method, and obtains the time response sequence of NGM model by using differential equation as a procedure for reasoning. It performs a numerical demonstration on the prediction accuracy of NGM model and its optimized models. As shown in the results, the proposed model and it optimized model can enhance the prediction accuracy. Numerical results illustrate that the proposed NGM model and its optimized model are effective. They are suitable for predicting the data sequence with the characteristics of non-homogeneous exponential law. This work makes important contribution to the enrichment of grey prediction theory.     

Non-linear model parameter estimation–estimating a feasible parameter set with respect to model use

This article deals with non-linear model parameter estimation from experimental data. As for non-linear models a rigorous identifiability analysis is difficult to perform, parameter estimation is performed in such a way that uncertainty in the estimated parameter values is represented by the range of model use results when the model is used for a certain purpose. Using this approach, the article presents a simulation study where the objective is to discover whether the estimation of model parameters can be improved, so that a small enough range of model use results is obtained. The results of the study indicate that from plant measurements available for the estimation of model parameters, it is possible to extract data that are important for the estimation of model parameters relative to a certain model use. If these data are improved by a proper measurement campaign (e.g. proper choice of measured variables, better accuracy, higher measurement frequency) it is to be expected that a valid model for a certain model use will be obtained. The simulation study is performed for an activated sludge model from wastewater treatment, while the estimation of model parameters is done by Monte Carlo simulation.     

The non-Markovian nature of nested logit choice

Discrete choice models are widely used for understanding how customers choose between a variety of substitutable goods. We investigate the relationship between two well studied choice models, the Nested Logit (NL) model and the Markov choice model. Both models generalize the classic Multinomial Logit model and admit tractable algorithms for assortment optimization. Previous evidence indicates that the NL model may be well approximated by, or be a special case of, the Markov model. We establish that the Nested Logit model, in general, cannot be represented by a Markov model. Further, we show that there exists a family of instances of the NL model where the choice probabilities cannot be approximated to within a constant error by any Markov choice model.     

基于遗传算法的附随扩散模型的构建及实证研究

论文研究了具有附随扩散关系的产品扩散特点,以Bass模型为理论基础,构建了附随扩散模型,然后以移动上网用户附随移动用户扩散的实例为研究对象,建立了移动上网附随扩散模型,并采用遗传算法估算模型参数,对今后几年移动上网用户的扩散规律进行了预测。最后,将附随扩散模型的研究结果与采用传统Bass模型、Logistic模聊的结果进行了对比,得出了附随扩散模型拟合和预测效果更好的结论。     

Four equivalent lot-sizing models

We study the following lot-sizing models that recently appeared in the literature: a lot-sizing model with a remanufacturing option, a lot-sizing model with production time windows, and a lot-sizing model with cumulative capacities. We show the equivalence of these models with a classical model: the lot-sizing model with inventory bounds.     

A Hessian Regularized Nonlinear Time Series Model

We introduce a nonparametric nonlinear time series model. The novel idea is to fit a model via penalization, where the penalty term is an unbiased estimator of the integrated Hessian of the underlying function. The underlying model assumption is very general: it has Hessian almost everywhere in its domain. Numerical experiments demonstrate that our model has better predictive power: if the underlying model complies with an existing parametric/semiparametric form (e.g., a threshold autoregressive model (TAR), an additive autoregressive model (AAR), or a functional coefficient autoregressive model (FAR)), our model performs comparably; if the underlying model does not comply with any preexisting form, our model outperforms in nearly all simulations. We name our model a Hessian regularized nonlinear model for time series (HRM). We conjecture on theoretical properties and use simulations to verify. Our method can be viewed as a way to generalize splines to high dimensions (when the number of variates is more than three), under which an analogous analytical derivation cannot work due to the curse of dimensionality. Supplemental materials are provided, and will help readers reproduce all results in the article.     

Iterative identification and control design using finite-signal-to-noise models

In this paper, a model is said to be validated for control design if using the model-based controller, the closed loop performance of the real plant satisfies a specified performance bound. To improve the model for control design, only closed loop response data is available to deduce a new model of the plant. Hence the procedure described herein involves three steps in each iteration: (i) closed loop identification; (ii) plant model extraction from the closed loop model; (iii) controller design. Thus our criteria for model validation involve both the control design procedure by which the closed loop system performance is evaluated, and the identification procedure by which a new model of the plant is deduced from the closed loop response data. This paper proposes new methods for both parts, and also proposes an iterative algorithm to connect the two parts. To facilitate both the identification and control tasks, the new finite-signal-to-noise (FSN) model of linear systems is utilized. The FSN model allows errors in variables whose noise covariances are proportional to signal covariances. Allowing the signal to noise ratios to be bounded but uncertain, a control theory to guarantee a variance upper bound is developed for the discrete version of this new FSN model. The identification of the closed loop system is accomplished by a new type of q-Markov Cover, adjusted to accommodate the assumed FSN structure of the model. The model of the plant is extracted from the closed loop identification model. This model is then used for control design and the process is repeated until the closed loop performance validates the model. If the iterations produce no such a controller, we say that this specific procedure cannot produce a model valid for control design and the level of the required performance must be reduced.     

Pseudo bond graph tunnel greenhouse model with accurate longwave/shortwave radiations model

This research work is focused on the construction of an accurate longwave/shortwave radiation model on a tunnel greenhouse pseudo-bond graph model, widely used in Tunisia. This model includes sun position, useful incoming solar radiation model, sky longwave radiation model, inside longwave and shortwave radiation model. The key idea is to use bond graphs allowing a lumped modelling approach which is suitable for control applications. Furthermore, an evaluation of some longwave radiative model components was made, noting that these components are particularly sensitive regarding to the thermal behaviour of the model.

Experimental tunnel greenhouse data are used as validation elements for the present model with globally good results. A comparative study was also performed between the present model and a previous bond graph model containing a simplistic radiative model. Practical simulation results show a clear improvement compared with the previous model.