A MATLAB program for estimation of unsaturated hydraulic soil parameters using an infiltrometer technique

We combined an inverse routine for assessing the hydraulic soil parameters of the Campbell/Mualem model with the power series solution developed by Philip for describing one-dimensional vertical infiltration into a homogenous soil. We based the estimation routine on a proposed measurement procedure especially suitable for early-time infiltrometer experiments where the flow can be considered as one-dimensional. The routine requires input of the initial soil water content and cumulative infiltration in two experiments with different pressures at the upper boundary and/or initial conditions. An independent measurement of the soil water content at saturation may reduce the uncertainty of estimated parameters. Response surfaces of the objective function were analysed. Scenarios for various soils and conditions, using numerically generated synthetic cumulative infiltration data with normally distributed errors, show promising results for finding the true values of the optimized parameters. We also investigated the effects of measurement frequency for the cumulative infiltration and errors in water content determinations.     

Computing arrival cost parameters in moving horizon estimation using sampling based filters

Moving horizon estimation (MHE) is a numerical optimization based approach to state estimation, where the joint probability density function (pdf) of a finite state trajectory is sought, which is conditioned on a moving horizon of measurements. The joint conditional pdf depends on the a priori state pdf at the start of the horizon, which is a prediction pdf based on historical data outside the horizon. When the joint pdf is maximized, the arrival cost is a penalty term based on the a priori pdf in the MHE objective function. Traditionally, the a priori pdf is assumed as a multivariate Gaussian pdf and the extended Kalman filter (EKF) and smoother are used to recursively update the mean and covariance. However, transformation of moments through nonlinearity is poorly approximated by linearization, which can result in poor initialization of MHE. Sampling based nonlinear filters completely avoid Taylor series approximations of nonlinearities and attempt to approximate the non-Gaussian state pdf using samples and associated weights or probability mass points. The performance gains of sampling based filters over EKF motivate their use to formulate the arrival cost in MHE. The a priori mean and covariance are more effectively propagated through nonlinearities and the resulting arrival cost term can help to keep the horizon small. It is also possible to find closed-form approximations to the non-Gaussian a priori pdf from the sampling based filters. Thus, more realistic nonparametric arrival cost terms can be included by avoiding the Gaussian assumption. In this paper the use of the deterministic sampling based unscented Kalman filter, the class of random sampling based particle filter and the aggregate Markov chain based cell filter are discussed for initializing MHE. Two simulation examples are included to demonstrate the benefits of these methods over the traditional EKF approach.     

Optimal instrumental variable multistep algorithms for estimation of the AR parameters of an ARMA process

Multistep implementations are derived for the optimal instrumental variable (OIV) estimators introduced by Stoica et al. in 1985. The proposed algorithms provide asymptotically efficient estimates of the AR parameters of an ARMA process. The computational complexity of these algorithms is modest compared with the maximum likelihood estimator. The performance of the OIV algorithms is illustrated by some numerical examples.     

Optimal sampling for estimation with constrained resources using a learning automaton-based solution for the nonlinear fractional knapsack problem

While training and estimation for Pattern Recognition (PR) have been extensively studied, the question of achieving these when the resources are both limited and constrained is relatively open. This is the focus of this paper. We consider the problem of allocating limited sampling resources in a “real-time” manner, with the explicit purpose of estimating multiple binomial proportions (the extension of these results to non-binomial proportions is, in our opinion, rather straightforward). More specifically, the user is presented with ‘n’ training sets of data points, S ₁,S ₂,…,S _n , where the set S _i has N _i points drawn from two classes {ω ₁,ω ₂}. A random sample in set S _i belongs to ω ₁ with probability u _i and to ω ₂ with probability 1?u _i , with {u _i }, i=1,2,…n, being the quantities to be learnt. The problem is both interesting and non-trivial because while both n and each N _i are large, the number of samples that can be drawn is bounded by a constant, c. A web-related problem which is based on this model (Snaprud et al., The Accessibility for All Conference, 2003) is intriguing because the sampling resources can only be allocated optimally if the binomial proportions are already known. Further, no non-automaton solution has ever been reported if these proportions are unknown and must be sampled. Using the general LA philosophy as a paradigm to tackle this real-life problem, our scheme improves a current solution in an online manner, through a series of informed guesses which move towards the optimal solution. We solve the problem by first modelling it as a Stochastic Non-linear Fractional Knapsack Problem. We then present a completely new on-line Learning Automata (LA) system, namely, the Hierarchy of Twofold Resource Allocation Automata (H-TRAA), whose primitive component is a Twofold Resource Allocation Automaton (TRAA), both of which are asymptotically optimal. Furthermore, we demonstrate empirically that the H-TRAA provides orders of magnitude faster convergence compared to the Learning Automata Knapsack Game (LAKG) which represents the state-of-the-art. Finally, in contrast to the LAKG, the H-TRAA scales sub-linearly. Based on these results, we believe that the H-TRAA has also tremendous potential to handle demanding real-world applications, particularly those dealing with the world wide web.     

Optimal selection of sampling locations and c-chart parameters

This paper presents a method for designing a statistical process contral program over the attributes of a multi-stage discrete part manufacturing system. The objective of the program is to minimize the sum of the costs of bad quality, inspection, searching for special causes of variation, process adjustment, and reworking of rejected items. The costs are affected by two interacting levels of decision, macro and micro. The macro level selects the points in the process at which quality control is exercised, referred to here as quality control windows. The micro level determines the control chart parameters, namely frequency of sampling, the sample size, and the chart range.     

Optimal adaptive control for estimation of parameters of ARX models

The objective of this paper is to find an adaptive control strategy which would enable us to estimate the parameters of the ARX model as accurately as possible while keeping the output of the system below a specified level of variability. It turns out that this optimal experiment design problem can be solved by using the Astrom-Wittenmark self-tuning tracker (1973) with a specific choice of the reference signal     

一种用于最优路径规划的改进遗传算法

设计了一种专门适用于路径规划的改进遗传算法．该算法根据最短路径问题的具体要求，将传统遗传算法中的编码、交叉、变异等操作均重新进行了定义，并且在进化过程中还能够自适应调节交叉和变异概率．仿真研究表明：改进的遗传算法具有较快的搜索速度和较高的搜索成功率，为实际车载导航系统中最优路径规划问题的解决提供了一种新方法．     

一种求解最优路径的改进遗传算法

在应用遗传算法进行路径规划时,本文针对遗传算法的＂收敛盲目性＂和＂收敛速度慢＂两个难题,结合模拟退火算法对适应度函数进行改进,结合禁忌搜索对变异算子进行改进,并且在进化过程中使用改进的自适应方法调节交叉概率与变异概率。算法的分析和测试表明,本文算法的改进是有效的。     

求解k条最优路径问题的遗传算法

文章提出的任意两点间k条最优路径问题的遗传算法,采用节点的自然路径作为染色体编码,根据路径节点的连接实施染色体的交叉操作,将节点路径块作为染色体的变异基因块实施变异操作。算法结构简明,收敛速度快,可应用于求解大规模网络中的多条最优路径问题。     

求解矩形件优化排样的自适应模拟退火遗传算法

矩形件优化排样是一个NPC问题,在工业界有着广泛的应用.针对该问题,提出一种自适应模拟退火遗传算法.采用一种基于环形交叉算子和环形变异算子的自适应遗传算法来自动调整交叉和变异概率;同时引入模拟退火算法对个体适应度大于平均适应度的个体进行退火处理.自适应模拟退火遗传算法充分发挥了自适应遗传算法与模拟退火算法各自的全局搜索能力与局部搜索能力.对比实验表明,该算法结合改进的最左最下布局算法解决矩形件优化排样问题更加有效.     

基于遗传算法的欠驱动机械臂优化控制研究

欠驱动系统是一类控制器少于系统自由度的二阶非完整系统。提出一种基于遗传算法优化的摇起控制器设计。该控制器主要用于控制Acrobot系统的摇起运动。首先运用部分反馈线性化的状态反馈控制设计摇起控制器,然后利用遗传算法对摇起控制器的三个参数进行在线自调整优化,以确保Acrobot摇起的动态性能达到最优化,为平滑而稳定地切换到平衡控制器提供保障。计算机仿真结果显示,该方法能大大缩短系统摇起到进入平衡状态的时间,并具有良好的动态性能。     

Optimal multicompartmental sampling designs for parameter estimation: Practical aspects of the identification problem

A computer program is described for the determination of optimal multicompartmental sampling designs as a handle on numerical identifiability. A design measure approach is used which automatically chooses the number of sample times and sites of sampling. A two-input four-pool output example is presented.     

Optimal field sampling for targeting minerals using hyperspectral data

This paper presents a statistical method for deriving optimal spatial sampling schemes. It focuses on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques were applied to obtain rule mineral images. Each pixel in these rule images represents the similarity between the corresponding pixel in the hyperspectral image to a reference spectrum. The rule images provide weights that are utilized in objective functions of the sampling schemes which are optimized through a process of simulated annealing. A HyMAP 126-channel airborne hyperspectral data acquired in 2003 over the Rodalquilar area in Spain serves as an application to target those pixels with the highest likelihood of occurrence of a specific mineral and as a collection the location of these sampling points selected represent the distribution of that particular mineral. In this area, alunite being a predominant mineral in the alteration zones was chosen as the target mineral. Three weight functions are defined to intensively sample areas where a high probability and abundance of alunite occurs. Weight function I uses binary weights derived from the SAM classification image, leading to an even distribution of sampling points over the region of interest. Weight function II uses scaled weights derived from the SAM rule image. Sample points are arranged more intensely in areas of abundance of alunite. Weight function III combines information from several different rule image classifications. Sampling points are distributed more intensely in regions of high probable alunite as classified by both SAM and SFF, thus representing the purest of pixels. This method leads to an efficient distribution of sample points, on the basis of a user-defined objective.     

HMM参数估计的Gibbs抽样算法

隐马氏模型（HMM）的参数估计是隐马氏模型各种应用的关键。经典的Baum-Welch算法容易陷入局部最优,对初始参数的要求苛刻。HMM参数估计的Gibbs抽样法,充分利用模型先验信息,借助马氏链蒙特卡洛方法（MCMC）的强大计算功能,避免了陷入局部最优,有更好的效果。     

模糊逻辑系统的遗传算法优化方法

参照模糊逻辑系统的表格查询学习算法,提出了一个切实可行的遗传算法优化方法。仿真结果表明,利用该方法可以在缺乏先验知识的情况下,只需要较少的样本数据,就可以通过评价学习,使优化后的模糊逻辑系统最大程度地拟合原系统的动态特性。     

Optimal estimation for the satellite attitude using star tracker measurements

An optimal estimation scheme is presented, which determines the satellite attitude using the gyro readings and the star tracker measurements of a commonly used satellite attitude measuring unit. The scheme is mainly based on the exponential Fourier densities that have the desirable closure property under conditioning. By updating a finite and fixed number of parameters, the conditional probability density, which is an exponential Fourier density, is recursively determined. Simulation results indicate that the scheme is more accurate and robust than extended Kalman filtering. It is believed that this approach is applicable to many other attitude measuring units. As no linearization and approximation are necessary in the approach, it is ideal for systems involving high levels of randomness and/or low levels of observability and systems for which accuracy is of overriding importance.     

离散时间线性系统最优控制的遗传算法实现

本文提出了求解控制变量受区间约束情形的离散时间线性系统最优控制的遗传算法,在遗传算法框架下给出了离散时间线性系统最优控制问题可行解的编码及初始化方法,设计了选择、交叉、变异等遗传算子,并对初始化方法及各种遗传算子的可行性给出理论分析。     

Optimal estimation in distributed processes using the innovations approach

An application of the innovations approach for tackling the state estimation problem for a class of linear distributed parameter systems is considered. This, in effect, leads to a unified treatment of filtering, smoothing, and prediction problems.     

Optimal estimation of linear discrete-time systems with stochastic parameters

This paper considers optimal linear state estimation in the general case of linear discrete-time systems with stochastic parameters which are statistically independent with respect to time. The estimator is derived by transforming the system to one with deterministic parameters and state dependent additive system and observation noise. It is shown that mean square stability of the system is a sufficient and almost necessary condition for the existence, uniqueness and stability of the time invariant estimator.