基于BP网络的广义预测控制的研究与应用

本文提出了一种基于BP神经网络的广义预测控制算法,利用BP神经网络求取广义预测控制的最优控制律,对广义预测控制算法进行了简化,使广义预测控制适用于快速控制系统。选用倒立摆系统作为研究的对象,将新算法应用于倒立摆的控制,取得了较好的控制效果,验证了方案的可行性。     

基于纯电动车电子机械制动系统的永磁同步电机控制

全球汽车行业正在加速发展,汽车向"电动化"、"智能化"、"共享化"的方向发展.纯电动车是汽车未来的发展趋势,制动系统又极为重要,因此有必要对纯电动车的制动系统进行研究.本文主要对电子机械制动系统的永磁同步电机控制进行了研究,对永磁同步电机建立数学模型,采用PI控制方法对永磁同步电机的位移环、速度环和电流环进行控制.仿真...     

基于BP神经网络的服装出口预测

服装出口贸易受到国内国际诸多因素的影响,是一个复杂的非线性系统。BP神经网络能够以任意精度逼近任何一个具有有限间断点的非线性函数,特别适合于解决非线性系统的预测决策问题。采用三层BP神经网络对我国服装出口进行预测,结果表明该方法能有效的对服装出口进行预测。     

利用改进的卡尔曼滤波器预测退化设备可靠性

针对退化设备可靠性预测的问题,采用加入次优渐消因子的卡尔曼滤波器预测设备未来时刻退化量均值,进而预测未来时刻的可靠性。本文给出了退化设备的可靠性预测方法和具体算法,这可为确定停机维护时间、维修费用等提供依据,具有重要意义。仿真实验验证了该方法的有效性。     

电力系统负荷预测方法的探讨

负荷预测是指在充分考虑一些重要的系统运行特点、增容决策、自然条件和社会影响的条件下,研究出一套可以用系统处理过去与未来负荷的数学方法,在满足一定精度要求的前提下,确定某特定时刻的负荷值。文章介绍几种较为适用的预测方法。     

基于神经网络的药品需求组合预测研究与应用

药品的市场需求预测是制药企业生产控制中的重要组成部分,具有复杂的非线性特点。本文以制药企业的药品需求预测为研究对象,通过分析药品需求的特征,建立了基于神经网络的组合预测模型。本文选择3种具有互补特征的神经网络预测方法（BP神经网络的预测方法、RBF神经网络的预测方法和GRNN广义回归神经网络）分别对药品需求进行预测,然后在此基础上使用平均绝对相对误差（MAPE）为最优准则,通过求解二次规划问题得到权重并按照一定的规则进行变权,从而建立了基于神经网络的药品需求组合预测模型,最后对该模型实际应用的精度和稳定性进行评价。实验表明,本方法能够提高预测精度、稳定性,并扩大了模型的适用范围。     

利用改进的卡尔曼滤波器预测退化设备可靠性

针对退化设备可靠性预测的问题,采用加入次优渐消因子的卡尔曼滤波器预测设备未来时刻退化量均值,进而预测未来时刻的可靠性。本文给出了退化设备的可靠性预测方法和具体算法,这可为确定停机维护时间、维修费用等提供依据,具有重要意义。仿真实验验证了该方法的有效性。     

倒立摆的模糊PID控制

本文在研究了倒立摆的系统构成的基础上分析得到了其动力学模型,并通过线性化处理推导出倒立摆在工作点附近的线性模型。通过对影响倒立摆稳定因素的分析,提出一种模糊PID的控制策略。该控制策略包括3个环路。通过理论分析和反复调整,本文得到了3个环路控制器的优化参数。最后,通过Matlab仿真和硬件在环实验,模糊PID控制的有效性将会得到验证。     

时滞系统的预测控制及其仿真分析

针对PID对大滞后大惯性系统的控制效果不佳、参数整定复杂等方面的不足,将预测控制应用到时滞控制系统当中,并在MATLAB环境下进行仿真。以算例证明预测控制应用于时滞控制系统中,比单纯的HD控制具有更良好的鲁棒性和抗干扰性,不但调节速度更快,参数整定方法更简单,而且还不依赖于对象模型的参数,非常适用于工业过程控制。     

基于BP神经网络的西安市宏观经济预测

宏观经济系统是一个复杂的非线性系统,对宏观经济进行预测应采用非线性的工具进行建模。采用BP神经网络对西安市宏观经济指标进行预测,此预测模型只需少量训练样本就可以确定网络的权值和阈值。实验表明模型预测精度高,能够对西安市宏观经济系统中的非线性关系进行描述,使建立的非线性模型与实际系统更加接近。     

Totally positive regression: E-optimal designs

E-optimality of approximate designs in linear regression models is paired with a dual problem of nonlinear Chebyshev approximation. When the regression functions form a totally positive system, then the information matrices of designs for subparameters turn out to be “almost” totally positive, a property which allows to solve the nonlinear Chebyshev problem. Thereby we obtain explicit formulae for E-optimal designs in terms of equi-oscillating generalized polynomials. The considerations unify and generalize known results on E-optimality for particular regression setups.     

Estimating and Forecasting with a Dynamic Spatial Panel Data Model*

This study focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006) , a dynamic spatial generalized method of moments (GMM) estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the spatial autoregressive (SAR) error model. The main idea is to mix non‐spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non‐spatial estimators and illustrate our approach with an application to new economic geography.     

Strong Consistency of the Maximum Likelihood Estimator in Generalized Linear and Nonlinear Mixed-Effects Models

Generalized linear and nonlinear mixed-effects models are used extensively in biomedical, social, and agricultural sciences. The statistical analysis of these models is based on the asymptotic properties of the maximum likelihood estimator. However, it is usually assumed that the maximum likelihood estimator is consistent, without providing a proof. A rigorous proof of the consistency by verifying conditions from existing results can be very difficult due to the integrated likelihood. In this paper, we present some easily verifiable conditions for the strong consistency of the maximum likelihood estimator in generalized linear and nonlinear mixed-effects models. Based on this result, we prove that the maximum likelihood estimator is consistent for some frequently used models such as mixed-effects logistic regression models and growth curve models.     

An algorithm for the generalized transportation problem

This paper describes a new computational technique for solving spatial economic equilibrium problems which are generalizations of the classic transportation problem. This technique makes use of a type of algorithm which has been developed in recent years to compute Kakutani fixed points and solve related problems. Existing algorithms for the generalized transportation problem employ quadratic programming, and therefore require that demand and supply functions be linear. By contrast, the algorithm of this paper can handle demand and supply relationships which are nonlinear or even semi-continuous. It can also handle non-constant transport costs and various other complications. The technique is capable of yielding highly accurate solutions, and appears to be computationally efficient on problems of reasonable size.     

应用中心流形法对铁路车辆转向架运动稳定性的hopf分叉分析

文章通过对铁路车辆转向架蛇行运动稳定性的分叉进行研究,分析了车辆系统在不同分叉状况下对车辆安全性能的影响。运用中心流形方法研究6自由度的车辆转向架,考虑轮轨接触几何关系和轮轨相互作用力的非线性,抗蛇行减振器的非线性,其余悬挂参数为线性。对系统稳定性进行判定,为以后车辆的设计提供了一个较好的方法。     

一种基于主动视觉的非线性摄像机自标定

针对计算机视觉摄像机镜头畸变对精度的影响问题,提出了一种非线性摄像机标定方法。该方法是将非线性模型视为线性模型和畸变项的叠加,然后在主动视觉下利用线性模型下的正交运动的FOE(当物体或摄像机做纯平移运动时图像对应点连线的交点)点的关系,给出非线性模型参数的约束方程,从而实现非线性模型的摄像机自标定。模拟和真实图像实验均表明,该方法是有效的,具有一定的实用价值。     

Theorems on the decomposition of a large nonlinear convex separable economic system in the dual direction

Large mathematical programming problems often arise as the result of the economic planning process. When such a problem is not only large, but nonlinear as well, there is a need to make it more manageable by breaking it down into several smaller and more easily handled subproblems. The subproblems are solved separately with the coordination activity carried out by a master problem. A decomposition method can be seen as a dialogue between the master problem and the subproblems, where the flow of information back and forth between the former and the latters results in a series of approximations converging to the solution of the overall problem. Such a decomposition method was elaborated by Benders [1] for linear programmes and generalized to nonlinear convex separable programmes by Kronsjö [4] and by Geoffrion [3]. After considering our basic nonlinear programming problem from a two-stage minimization point of view, we review the Kronsjö nonlinear decomposition algorithm. Then we establish some properties of a function related to this algorithm.I am grateful to Professor T.O.M. Kronsjö for inspiring this work and giving valuable help.     

Tests for model specification in the presence of alternative hypotheses: Some further results

In Davidson and MacKinnon (1981), two of the present authors proposed a novel and very simple procedure for testing the specification of a nonlinear regression model against the evidence provided by a non-nested alternative. In this paper we extend their results in several directions. First, we relax a number of the assumptions of the previous paper; we admit the possibility that the nonlinear regression functions may depend on lagged dependent variables, and we do not require that the error terms be normally distributed. Second, we show how the earlier procedure may straightforwardly be generalized to the case where the two non-nested models involve different transformations of the dependent variable. Finally, we propose a simple procedure for testing non-nested linear regression models which have endogenous variables on the right-hand side, and have therefore been estimated by two-stage least squares.     

Joint and marginal specification tests for conditional mean and variance models

This article proposes a class of joint and marginal spectral diagnostic tests for parametric conditional means and variances of linear and nonlinear time series models. The use of joint and marginal tests is motivated from the fact that marginal tests for the conditional variance may lead to misleading conclusions when the conditional mean is misspecified. The new tests are based on a generalized spectral approach and do not need to choose a lag order depending on the sample size or to smooth the data. Moreover, the proposed tests are robust to higher order dependence of unknown form, in particular to conditional skewness and kurtosis. It turns out that the asymptotic null distributions of the new tests depend on the data generating process. Hence, we implement the tests with the assistance of a wild bootstrap procedure. A simulation study compares the finite sample performance of the proposed and competing tests, and shows that our tests can play a valuable role in time series modeling. Finally, an application to the S&P 500 highlights the merits of our approach.     

广义非线性轴向运动弦的参数分析

本文的研究对象是服从边界控制的非线性轴向运动弦。弦线的运动方程以及边界条件都是根据Hamilton原理得出的。Laypunov关于运动的稳定性理论,特别是他的"直接法",为非线性系统的稳定性分析提供了一个有效且方便的工具,已经成为近代控制论的一个坚实的基础。