最佳线性过滤器的时域综合法

本文提出一种最佳线性过滤器的时域综合方法,这个方法不仅可直接应用于以图表或数列给出的相关函数,更主要的可避免应用频率域法时为解决可实现性问题而遇到的困难。

最佳线性过滤器构成的一种自适应系统

本文介绍一种基于时域综合法的由最佳线性过滤器构成的自适应系统。文中首先介绍噪音为白色噪音时的可变传递函数和最佳自适应过滤器。由于在一般情况噪音并非是白色噪音,这时过滤器的构成就比较困难,文中给出了这种自适应最佳过滤器的组成。

某钢结构气象塔在时程风荷载作用下考虑P-Δ效应的动力时程分析

根据线性过滤器中的自回归模型,借助数值分析软件MATLAB编写了频域转换时域程序,模拟了场地上不同高度处的具有空间相关性的时程风荷载,然后利用有限元软件SAP2000对钢结构气象塔进行时程风荷载作用下动力时程分析,得到气象塔的时程位移、时程速度和时程加速度响应,并将静力风荷载位移响应与时程位移峰值响应进行对比,作了误差分析。

Kernel density estimation and marginalized-particle based probability hypothesis density filter for multi-target tracking

In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.

Network-Based H_∞ Filtering for Linear Systems with Randomly Varying Sensor Delay

An H∞ filter design for linear time delay system with randomly varying sensor delay is investigated.The delay considered here is assumed to satisfy a certain stochastic characteristic.A stochastic variable satisfying Bernoulli random binary distribution is introduced and a new system model is established by employing the measurements with random delay.By using the linear matrix inequality(LMI) technique,sufficient conditions are derived for ensuring the mean-square stochastic stability of the filtering error systems and guaranteeing a prescribed H∞ filtering performance.Finally,a numerical example is given to demonstrate the effectiveness of the proposed approach.

Robust H_∞ filtering for neutral jump system with time-delays and uncertainties

The problem of robust H∞ filtering for a class of neutral jump systems with time-delay and norm- bounded uncertainties is considered. By re-constructing the system, the dynamics of overall augmented error systems is obtained which involves unknown inputs represented by disturbances, model uncertainties and time-delays. As to the nominal system, sufficient conditions are provided for the existence of the mode-dependent H∞ filter by selecting the appropriate Lyapunov-Krasovskii function and the robust H∞ filter is proposed for the jump system while considering the time-delays and uncertainties. Both of above conditions for the existence of the H∞ filter and roust H∞ filter are presented in terms of linear matrix inequalities, and convex optimization problems are formulated to design the desired filters. By employing the proposed mode-dependent H∞ filter, the systems have the stochastic stability and better ability of restraining disturbances stochastically, and the given prescribed H∞ performance is guaranteed. Simulation resuhs illustrate the effectiveness of developed techniques.

Improving Fuel Diesel Efficiency Using Fuel Filter

The price of fuel oil continues to rise, decreased supplies oil at the other side, both factors increase the interest of researchers to conduct research related to fuel efficiency. Therefore the aim of this study is to improve the efficiency fuel of diesel engine using fuel filter. The method used for the research is testing the most efficient fuel filters made of coil wire coil winding 5,000, the three distinguished from the coil core diameter of 44.5, 28.5 and 17.5 mm in diesel engine. The performance test was conducted from 1,100 rpm to 1,700 rpm, the throttle opening of 30%-60%. The first testing was done by creating a constant speed 1,500 rpm and throttle opening varies from 30% to 60%, further testing is done by varying the rpm start from 1,100 rpm to 1,700 rpm to make constant valve 40% and 60%.

Observability Analysis and Navigation Algorithm for Distributed Satellites System Using Relative Range Measurements

The problem of navigation for the distributed satellites system using relative range mea- surements is investigated. Firstly, observability for every participating satellites is analyzed based on the nonlinear Keplerian model containing J2 perturbation and the nonlinear measurements. It is proven that the minimum number of tracking satellites to assure the observability of the distributed satellites system is three. Additionally, the analysis shows that the J2 perturbation and the nonlinearity make little contribution to improve the observability for the navigation. Then, a quasi-consistent extended Kalman filter based navigation algorithm is proposed, which is quasi-consistent and can provide an on- line evaluation of the navigation precision. The simulation illustrates the feasibility and effectiveness of the proposed navigation algorithm for the distributed satellites system.

A NONMONOTONE LINE SEARCH FILTER METHOD WITH REDUCED HESSIAN UPDATING FOR NONLINEAR OPTIMIZATION

This paper proposes a nonmonotone line search filter method with reduced Hessian updating for solving nonlinear equality constrained optimization.In order to deal with large scale problems,a reduced Hessian matrix is approximated by BFGS updates.The new method assures global convergence without using a merit function.By Lagrangian function in the filter and nonmonotone scheme,the authors prove that the method can overcome Maratos effect without using second order correction step so that the locally superlinear convergence is achieved.The primary numerical experiments are reported to show effectiveness of the proposed algorithm.

A NONMONOTONE FILTER LINE SEARCH TECHNIQUE FOR THE MBFGS METHOD IN UNCONSTRAINED OPTIMIZATION

This paper presents a new nonmonotone filter line search technique in association with the MBFGS method for solving unconstrained minimization.The filter method,which is traditionally used for constrained nonlinear programming(NLP),is extended to solve unconstrained NLP by converting the latter to an equality constrained minimization.The nonmonotone idea is employed to the filter method so that the restoration phrase,a common feature of most filter methods,is not needed.The global convergence and fast local convergence rate of the proposed algorithm are established under some reasonable conditions.The results of numerical experiments indicate that the proposed method is efficient.

A FILTER SECANT METHOD WITH NONMONOTONE LINE SEARCH FOR EQUALITY CONSTRAINED OPTIMIZATION

This paper proposes a filter secant method with nonmonotone line search for non-linearequality constrained optimization.The Hessian of the Lagrangian is approximated using the BFGSsecant update.This new method has more flexibility for the acceptance of the trial step and requires lesscomputational costs compared with the monotone one.The global and local convergence of the proposedmethod are given under some reasonable conditions.Further,two-step Q-superlinear convergence rateis established by introducing second order correction step.The numerical experiments are reported toshow the effectiveness of the proposed algorithm.