Joint probability density function of the stochastic responses of nonlinear structures

The joint probability density fimction （PDF） of different structural responses is a very important topic in the stochastic response analysis of nonlinear structures. In this paper, the probability density evolution method, which is successfully developed to capture the instantaneous PDF of an arbitrary single response of interest, is extended to evaluate the joint PDF of any two responses. A two-dimensional partial differential equation in terms of the joint PDF is established. The strategy of selecting representative points via the number theoretical method and sieved by a hyper-ellipsoid is outlined. A two-dimensional difference scheme is developed. The free vibration of an SDOF system is examined to verify the proposed method, and a flame structure exhibiting hysteresis subjected to stochastic ground motion is investigated. It is pointed out that the correlation of different responses results from the fact that randomness of different responses comes from the same set of basic random parameters involved. In other words, the essence of the probabilistic correlation is a physical correlation.

Shape control on probability density function in stochastic systems

A novel strategy of probability density function （PDF） shape control is proposed in stochastic systems. The control er is designed whose parameters are optimal y obtained through the improved particle swarm optimization algorithm. The parameters of the control er are viewed as the space position of a particle in particle swarm optimization algorithm and updated continual y until the control er makes the PDF of the state variable as close as possible to the expected PDF. The proposed PDF shape control technique is compared with the equivalent linearization technique through simulation experiments. The results show the superiority and the effectiveness of the proposed method. The control er is excellent in making the state PDF fol ow the expected PDF and has the very smal error between the state PDF and the expected PDF, solving the control problem of the PDF shape in stochastic systems effectively.

Linearized Controller Design for the Output Probability Density Functions of Non-Gaussian Stochastic Systems

This paper presents a linearized approach for the controller design of the shape of output probability density functions for general stochastic systems. A square root approximation to an output probability density function is realized by a set of B-spline functions. This generally produces a nonlinear state space model for the weights of the B-spline approximation. A linearized model is therefore obtained and embedded into a performance function that measures the tracking error of the output probability density function with respect to a given distribution. By using this performance function as a Lyapunov function for the closed loop system, a feedback control input has been obtained which guarantees closed loop stability and realizes perfect tracking. The algorithm described in this paper has been tested on a simulated example and desired results have been achieved.

The Many-on-One Stochastic Duel Model with Information-Sharing

In this paper we extend the one-on-one stochastic duel model with searching to the many-on-one case based on information-sharing. We have derived the probability density function of the time to kill the target in many-on-one model. It is illustrated by an example in which the firing time and the searching time are of different exponential distributions.

Technique of Probability Density Function Shape Control for Nonlinear Stochastic Systems

The shape control of probability density function(PDF) of the system state is an important topic in stochastic systems. In this paper, we propose a control technique for PDF shape of the state variable in nonlinear stochastic systems. Firstly, we derive and prove the form of the controller by investigating the Fokker-PlanckKolmogorov(FPK) equation arising from the stochastic system. Secondly, an approach for getting approximate solution of the FPK equation is provided. A special function including some parameters is taken as the approximate stationary solution of the FPK equation. We use nonlinear least square method to solve the parameters in the function, and capture the approximate solution of the FPK equation. Substituting the approximate solution into the form of the controller, we can acquire the PDF shape controller. Lastly, some example simulations are conducted to verify the algorithm.

Adaptive Linear Filtering Design with Minimum Symbol Error Probability Criterion

Adaptive digital filtering has traditionally been developed based on the minimum mean square error （MMSE） criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate （MSER） criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sample-by-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.

基于紧支撑多变量多项式函数的非线性随机系统概率密度函数形状控制方法

针对非线性随机系统的概率密度函数(PDF)形状控制问题,文中以FokkerPlanck-Kolmogorov(FPK)方程为研究工具,提出了一种基于紧支撑多变量多项式(CSMP)函数的非线性随机系统PDF形状控制方法。当系统处于稳定状态时,系统的PDF被困在特定的紧凑子空间中,不需要对整个空间进行积分。而CSMP函数在一段连续的空间内非0,满足紧凑子空间的特征。因此,文中将CSMP的线性组合(CSMP-LC)作为FPK方程的稳态近似解逼近目标PDF。首先,采用飞蛾扑火优化(MFO)算法优化CSMP-LC函数的参数;然后,通过对多维稳态FPK方程的每一维状态变量进行积分,确保稳态FPK方程在整个空间中的积分为0;最后,求解出一维和二维非耦合的状态变量PDF形状控制器,并进行了仿真实验。结果表明,对于一维非线性随机系统,文中提出的方法能有效地实现对不同类型目标PDF形状(单峰形状、双峰形状、三峰形状)的控制,且在目标PDF形状为复杂的三峰时,文中方法的均值、方差、峰度和偏度误差均优于其他两种方法。文中方法扩展到二维状态变量非耦合的非线性随机系统时,也能较好地实现对PDF形状的控制,为多变量随机系统的PDF形状控制研究提供了新的思路。同时,CSMP函数可以减少积分计算的复杂性,降低了非线性随机系统的PDF形状控制器的求解难度。

Fault tolerant control based on stochastic distribution via RBF neural networks

A new fault tolerant control（FTC） via a controller reconfiguration approach for general stochastic nonlinear systems is studied.Different from the formulation of classical FTC methods,it is supposed that the measured information for the FTC is the probability density functions（PDFs） of the system output rather than its measured value.A radial basis functions（RBFs） neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network.As a result,the nonlinear FTC problem subject to dynamic relation between the input and the output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural network approximation to the output PDFs.The FTC design consists of two steps.The first step is fault detection and diagnosis（FDD）,which can produce an alarm when there is a fault in the system and also locate which component has a fault.The second step is to adapt the controller to the faulty case so that the system is able to achieve its target.A linear matrix inequality（LMI） based feasible FTC method is applied such that the fault can be detected and diagnosed.An illustrated example is included to demonstrate the efficiency of the proposed algorithm,and satisfactory results have been obtained.

Studies on Stochastic Parametric Roll of Ship with Stochastic Averaging Method

The paper studies the parametric stochastic roll motion in the random waves.The differential equation of the ship parametric roll under random wave is established with considering the nonlinear damping and ship speed.Random sea surface is treated as a narrow-band stochastic process,and the stochastic parametric excitation is studied based on the effective wave theory.The nonlinear restored arm function obtained from the numerical simulation is expressed as the approximate analytic function.By using the stochastic averaging method,the differential equation of motion is transformed into Ito’s stochastic differential equation.The steady-state probability density function of roll motion is obtained,and the results are validated with the numerical simulation and model test.

Probability density function solution to nonlinear ship roll motion excited by external Poisson white noise

The stationary probability density function (PDF) solution to nonlinear ship roll motion excited by Poisson white noise is analyzed. Subjected to such random excitation, the joint PDF solution to the roll angle and angular velocity is governed by the generalized Fokker-Planck-Kolmogorov (FPK) equation. To solve this equation, the exponential-polynomial closure (EPC) method is adopted. With the EPC method, the PDF solution is assumed to be an exponential-polynomial function of state variables. Special measure is taken such that the generalized FPK equation is satisfied in the average sense of integration with the assumed PDF. The problem of determining the unknown parameters in the approximate PDF finally results in solving simultaneous nonlinear algebraic equations. Both slight and high nonlinearities are considered in the illustrative examples. The analysis shows that when a second-order polynomial is taken, the result of the EPC method is the same as the one given by the equivalent linearization (EQL) method. The EQL results differ significantly from the simulated results in the case of high nonlinearity. When a fourth-order or sixth-order polynomial is taken, the results of the EPC method agree well with the simulated ones, especially in the tail regions of the PDF. This agreement is observed in the cases of both slight and high nonlinearities.

A Novel PDF Shape Control Approach for Nonlinear Stochastic Systems

In this work,a novel shape control approach of the probability density function(PDF)for nonlinear stochastic systems is presented.First,we provide the formula for the PDF shape controller without devising the control law of the controller.Then,based on the exact analytical solution of the Fokker-PlanckKolmogorov(FPK)equation,the product function of the polynomial and the exponential polynomial is regarded as the stationary PDF of the state response.To validate the performance of the proposed control approach,we compared it with the exponential polynomial method and the multi-Gaussian closure method by implementing comparative simulation experiments.The results show that the novel PDF shape control approach is effective and feasible.Using an equal number of parameters,our method can achieve a similar or better control effect as the exponential polynomial method.By comparison with the multiGaussian closure method,our method has clear advantages in PDF shape control performance.For all cases,the integral of squared error and the errors of first four moments of our proposed method were very small,indicating superior performance and promising good overall control effects of our method.The approach presented in this study provides an alternative for PDF shape control in nonlinear stochastic systems.

Stochastic Distribution Control of Singular Systems: Output PDF Shaping

This paper presents a new algorithm designed to control the shape of the output probability density function (PDF) of singular systems subjected to non-Gaussian input. The aim is to select a control input uk such that the output PDF is made as close as possible to a given PDF.Based on the B-spline neural network approximation of the output PDF, the control algorithm is formulated by extending the developed PDF control strategies of non-singular systems to singular systems. It has been shown that under certain conditions the stability of the closed-loop system can be guaranteed. Simulation examples are given to show the effectiveness of the proposed control algorithm.

Stochastic response analysis of noisy system with non-negative real-power restoring force by generalized cell mapping method

The stochastic response of a noisy system with non-negative restoring force is investigated. The generalized cell mapping （GCM） method compute the transient and stationary probability density functions （PDFs） real-power is used to Combined with the global properties of the noise-free system, the evolutionary process of the tran- sient PDFs is revealed. The results show that stochastic P-bifurcation occurs when the system parameter varies in the response analysis and the stationary PDF evolves from bimodal to unimodal along the unstable manifold during the bifurcation.

Nonlinear Properties of the Rice Statistical Distribution: Theory and Applications in Stochastic Data Analysis

The paper considers the theoretical basics and the specific mathematical techniques having been developed for solving the tasks of the stochastic data analysis within the Rice statistical model in which the output signal’s amplitude is composed as a sum of the sought-for initial value and a random Gaussian noise. The Rician signal’s characteristics such as the average value and the noise dispersion have been shown to depend upon the Rice distribution’s parameters nonlinearly what has become a prerequisite for the development of a new approach to the stochastic Rician data analysis implying the joint signal and noise accurate evaluation. The joint computing of the Rice distribution’s parameters allows efficient reconstruction of the signal’s in-formative component against the noise background. A meaningful advantage of the proposed approach consists in the absence of restrictions connected with any a priori suppositions inherent to the traditional techniques. The results of the numerical experiments are provided confirming the efficiency of the elaborated approach to stochastic data analysis within the Rice statistical model.

A Stochastic Optimal Control Theory to Model Spontaneous Breathing

Respiratory variables, including tidal volume and respiratory rate, display significant variability. The probability density function (PDF) of respiratory variables has been shown to contain clinical information and can predict the risk for exacerbation in asthma. However, it is uncertain why this PDF plays a major role in predicting the dynamic conditions of the respiratory system. This paper introduces a stochastic optimal control model for noisy spontaneous breathing, and obtains a Shrödinger’s wave equation as the motion equation that can produce a PDF as a solution. Based on the lobules-bronchial tree model of the lung system, the tidal volume variable was expressed by a polar coordinate, by use of which the Shrödinger’s wave equation of inter-breath intervals (IBIs) was obtained. Through the wave equation of IBIs, the respiratory rhythm generator was characterized by the potential function including the PDF and the parameter concerning the topographical distribution of regional pulmonary ventilations. The stochastic model in this study was assumed to have a common variance parameter in the state variables, which would originate from the variability in metabolic energy at the cell level. As a conclusion, the PDF of IBIs would become a marker of neuroplasticity in the respiratory rhythm generator through Shr?dinger’s wave equation for IBIs.

随机场TPDF在非结构网格平台的开发及检验

为实现对燃烧室内部燃烧过程的高精度数值模拟,增强航空发动机燃烧室研究手段,采用开源的非结构网格平台Saturne,开发可以耦合详细化学反应机理,高精度解析湍流燃烧过程的概率密度函数输运方程(TPDF)湍流燃烧模型。采用随机场方法求解TPDF方程,在原有程序基础上研发了TPDF程序模块及配合的加速算法等模拟单元,发展了针对燃烧室燃烧过程模拟的软件功能。采用射流火焰Flame D和旋流火焰TECFLAM对新软件进行了测试,结果表明:模拟结果与试验数据趋势一致,精度尚可。对某型燃烧室性能模拟验证中,计算的出口温度分布与试验结果一致,平均温度误差小于3.8%,新的软件可进一步应用于燃烧室性能的研究。

基于神经PID控制的随机分布系统的研究

针对随机分布系统控制及干扰抑制问题,提出一种基于神经PID控制算法。利用神经网络的自适应和自学习能力在线调整链接权值,实现PID控制器三个参数的整定;同时,在建立状态反馈系统基础上,通过线性矩阵不等式(LMI)给出控制器的设计方法,并引入H∞范数,以提高系统的抗干扰能力,实现系统输出概率密度函数(PDF)追踪目标概率密度函数的控制。计算机仿真结果表明,该算法可实现系统输出概率密度函数追踪目标概率密度函数,抑制追踪控制中的干扰,并确保系统具有满意的鲁棒性和稳定性。

基于路径积分法的输液管道随机动态响应分析

随机激励下的输液管道在工程上广泛存在,对其进行研究具有十分重要的意义.为了预测高斯白噪声激励下输液管道系统的随机动态响应,基于哈密顿原理建立了高斯白噪声激励下非线性输液管道的动力学模型.采用Galerkin截断方法对输液管道的控制方程进行离散化.采用基于Gauss-Legendre公式的路径积分法计算了输液管道随机振动响应的位移概率密度函数和速度概率密度函数.采用Monte Carlo方法与路径积分法得到的计算结果进行对比,验证了路径积分法在计算输液管道振动响应上具有较高的计算精度.研究了流速、激励强度和阻尼系数对输液管道位移概率密度函数和速度概率密度函数的影响,并确定了输液管道位移概率密度函数出现双峰时的临界流速.结果表明,采用路径积分法计算输液管道系统的动态响应是有效的.流速增大会使系统可能发生的最大位移变大,可能发生的最大速度不变;激励强度增大会使系统可能发生的最大位移和最大速度变大;阻尼系数增大会使系统可能发生的最大位移和最大速度变小.此外,研究发现流速增大是诱导输液管道发生随机分岔的因素之一.

含有摇摆装置的双层桥墩结构随机振动分析

基于一种下层含有摇摆装置的双层桥墩结构模型,建立了系统的随机动力学系统模型,其中随机激励选用高斯白噪声模型,自复位恢复力采用经典旗帜形模型。运用广义谐波平衡法将旗帜形滞回力近似分解为幅值依赖的等效拟线性弹性力和拟线性阻尼力,获得原系统的等效随机系统;采用标准随机平均法理论,将等效系统近似为关于幅值的平均伊藤随机微分方程,建立并求解与之对应的Fokker‑Planck‑Kolmogorov(FPK)方程获得系统的稳态响应。探讨系统参数,如能量耗散系数等对系统稳态响应的影响,并通过Monte Carlo数值模拟加以验证。另外,借助Laplace变换,得到等效系统的转换函数及条件功率谱密度,结合下层幅值的稳态概率密度,得到下层幅值响应的功率谱密度估计。

科尔沁沙地土壤水分动态分析及其概率密度函数模拟

基于Laio土壤水分动态随机模型(Laio模型),利用2006—2010年5～9月土壤水分连续监测数据及日降水资料,分析科尔沁沙地固定沙丘和沙质草地生长季根系层土壤水分动态及其与降水格局的关系,研究点尺度土壤水分概率密度函数,并对Laio模型涉及的13个参数进行了敏感性分析。结果表明:①研究区年降水的季节分配极不均匀,主要集中在4～10月的生长季,占全年降水量的93%;0～5 mm降水事件占全年降水事件的73%,但其降水量只占全年降水量的25%;降水间隔期以0～10 d为主,占全年无降水期的38%,其频数最高,占全年间隔期频数的87%。②固定沙丘和沙质草地根系层厚度分别为0～100 cm和0～70 cm,沙质草地根系层土壤水分显著高于固定沙丘;两类沙地7月份的土壤水分都显著高于生长季其他月份。③两类沙地生长季根系层土壤水分均服从正态分布;通过Laio模型得到了两类沙地生长季根系层土壤水分概率密度函数p(s),其峰值及峰值出现的位置和峰的阔度均与观测结果很接近,说明Laio模型能对科尔沁沙地土壤水分概率密度函数进行较好的模拟。④Laio模型涉及的13个参数中,对p(s)最为敏感的参数是降水频率λ、平均降水量α、最大蒸散量Emax、水分胁迫点s*和凋萎系数sw,主要影响p(s)曲线的峰值。