基于飞行动力学和遗传算法的嫦娥三号月球软着陆仿真模拟

介绍了飞行动力学原理与遗传算法,将飞行动力学与遗传算法相结合,建立基于飞行动力学与遗传算法的嫦娥三号月球软着陆轨道模型,运用遗传算法对该轨道模型进行了优化,用Matlab软件对嫦娥三号月球软着陆轨道的优化曲线进行了仿真,并对其进行了分析,该软着陆模型比单一的飞行动力学模型更加科学和准确。     

遗传算法在VAV中央空调优化控制中的应用

以VAV中央空调能耗仿真模型为基础,根据VAV中央空调节能优化问题的特点,分析了利用遗传算法解决该问题的可行性。详细介绍了利用遗传算法寻找VAV中央空调系统运行过程中各个可控变量的最佳设定值的优化过程,并对遗传算法的运行效果进行了分析。建立了基于遗传算法的VAV中央空调控制仿真系统,对该方法在实际系统中的应用进行了仿真验证。     

随机化均匀设计混合遗传算法求解图的二划分问题

图的二划分问题是一个典型的NP—hard组合优化问题,在许多领域都有重要应用．近年来,传统遗传算法等各种智能优化方法被引入到该问题的求解中来,但效果不理想．基于理想浓度模型的机理分析,利用随机化均匀设计抽样的理论和方法,对遗传算法中的交叉操作进行了重新设计,并在分析图的二划分问题特点的基础上,结合局部搜索策略,给出了一个解决图的二划分问题的新的遗传算法．通过将该算法与简单遗传算法和佳点集遗传算法进行求解图的二划分问题的仿真模拟比较,可以看出新的算法提高了求解的质量、速度和精度．     

团购模式下云制造服务资源组合优化模型与算法

在云制造服务环境中,为了进一步降低需求者的服务成本,提出了一种团购模式下云制造服务资源组合优化模型与算法。在云制造平台发展的初期阶段,以服务需求者的视角分析云制造服务资源组合优化管理问题,通过团购模式研究了资源组合优化模型与算法,模型中考虑团购定价、团购信任度等关键影响因素,对云制造资源组合优化进行综合决策;设计改进的遗传算法进行模型求解,进一步对团购模式下云制造服务资源组合模型进行仿真分析。通过不同规模问题的仿真实验验证了模型与算法的有效性和可行性,仿真结果表明,在团购规模逐渐增大的情况下,团购模式比个体模式更具有成本优势。     

基于分布式遗传算法的水质传感器布置优化研究

水质传感器优化布置是指在城镇配水管网中最优位置布置水质传感器对污染物进行检测,从而达到监测预警的目的,其本质是一类大规模离散组合优化问题。首先从数学上对该问题进行分析,论证了其具有NP-Complete特性;然后针对该问题计算开销大等特点,提出了基于Spark云计算模型的分布式遗传算法;最后以一个典型的复杂配水管网为对象进行实验,仿真结果表明,所提出的算法不仅具有搜索速度快、精度高等优点,而且还具有较好的线性加速比。     

基于遗传算法的有限推力交会轨道优化仿真

针对有限推力空间飞行器交会对接逼近段燃料/时间组合最优轨道优化问题,提出了一种采用遗传算法求解的优化策略.以C-W方程为交会模型,将整个逼近段分为若干弧段,在每个弧段内追踪航天器均采用常值推力机动,这样可以求得C-W方程在每个弧段内的解析解,于是,交会对接逼近段的轨道优化问题可以转化为具有非线性约束的数学规划问题,最后采用广义拉格朗日-遗传算法对该问题进行了优化求解.数学仿真结果表明,该方法可以很好的解决交会对接终端逼近段燃料/时间组合最优轨道优化问题.     

拉丁超立方体抽样遗传算法求解图的二划分问题

图的二划分问题是一个典型的NP-hard组合优化问题, 在许多领域都有重要应用. 近年来, 传统遗传算法等各种智能优化方法被引入到该问题的求解中来, 但效果不理想. 基于理想浓度模型的机理分析, 利用拉丁超立方体抽样的理论和方法, 对遗传算法中的交叉操作进行了重新设计, 并在分析图二划分问题特点的基础上, 结合局部搜索策略, 给出了一个解决图二划分问题的新的遗传算法, 称之为拉丁超立方体抽样遗传算法. 通过将该算法与简单遗传算法和佳点集遗传算法进行求解图二划分问题的仿真模拟比较, 可以看出新的算法提高了求解的质量、速度和精度.     

基于混合神经网络的车辆路径模型研究

为有效解决现代物流配送中的车辆路径问题,发挥BP神经网络在解决分类问题和Hopfield神经网络在解决组合优化问题中的优势,依据"分而治之"策略提出了基于混合神经网络的优化模型。通过BP神经网络对一个配送中心范围内的多个配送点进行区域划分,在各子区域内使用Hopfield神经网络求得最优配送路径,从而得到质量较高的解和较快的收敛速度。基于Matlab的仿真实验结果表明,与传统的爬山算法、遗传算法相比,该模型能够获得性能更好的全局最优解。     

遗传算法在甲醛生产过程优化中的应用

遗传算法是一种模拟自然进化而提出的简单高效的组合优化算法。本文研究了甲醛生产过程的优化问题，该过程由于其反应动力学的固有复杂性而无法建模，本文表明遗传算法可以有效地解决这一过程的寻优问题。     

改进型量子遗传算法求解机器人联盟问题

联盟是多机器人之间一种重要的合作方法,如何生成面向某个任务的最优联盟是一个复杂的组合优化问题。引入量子遗传算法来解决这一问题,在求解过程中引入“基于信息正反馈的岛屿模型”对量子遗传算法进行改进,并采用进化方程对量子门进行更新,使其不再易于陷入局部极值。仿真实验结果表明,该算法在解的质量和收敛速度上优于目前同类算法。     

基于改进差分进化算法的非线性系统模型参数辨识

针对非线性模型的参数估计寻优较为困难的问题,提出一种基于改进的差分进化算法的非线性系统模型参数辨识新方法。通过引入一个自适应变异率,随着迭代的进行自适应调整缩放因子,从而在初期保持种群多样性以避免早熟,并在后期逐步降低变异率,保留优良信息,避免最优解遭到破坏。交叉概率采用动态非线性增加的方法,提高了收敛速度。为了验证算法性能,针对几类典型的非线性模型参数辨识问题进行了仿真研究,并将其应用于一类发酵动力学模型参数的估计中。结果表明改进算法的参数辨识精度高,收敛速度也比较快,有效提高了模型建立的精度与效率,为解决实际系统中参数估计问题提供了一条可行的途径。     

Application of parallel computing to stochastic parameter estimation in environmental models

Parameter estimation or model calibration is a common problem in many areas of process modeling, both in on-line applications such as real-time flood forecasting, and in off-line applications such as the modeling of reaction kinetics and phase equilibrium. The goal is to determine values of model parameters that provide the best fit to measured data, generally based on some type of least-squares or maximum likelihood criterion. Usually, this requires the solution of a non-linear and frequently non-convex optimization problem. In this paper we describe a user-friendly, computationally efficient parallel implementation of the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm for stochastic estimation of parameters in environmental models. Our parallel implementation takes better advantage of the computational power of a distributed computer system. Three case studies of increasing complexity demonstrate that parallel parameter estimation results in a considerable time savings when compared with traditional sequential optimization runs. The proposed method therefore provides an ideal means to solve complex optimization problems.     

Optimal input design for parameter estimation in a single and double tank system through direct control of parametric output sensitivities

In this paper the traditional and well-known problem of optimal input design for parameter estimation is considered. In particular, the focus is on input design for the estimation of the flow exponent present in Bernoulli's law. The theory will be applied to a water tank system with a controlled inflow and free outflow. The problem is formulated as follows: Given the model structure (f, g), which is assumed to be affine in the input, and the specific parameter of interest (θ), find a feedback law that maximizes the sensitivity of the model output to the parameter under different flow conditions in the water tank. The input design problem is solved analytically. The solution to this problem is used to estimate the parameter of interest with a minimal variance. Real-world experimental results are presented and compared with theoretical solutions.     

Optimum recursive filtering of noisy two-dimensional data with sequential parameter identification

A two-dimensional recursive estimation algorithm based on the asymmetric half-plane model is described for the problem of MMSE (minimum mean-square error) filtering. The optimum filtering problem is solved by formulating the asymmetric half-plane ARMA (autoregressive moving average) model for two-dimensional data. The sequential parameter identification from the noisy two-dimensional data is also discussed, utilizing the stochastic approximation. Experiments were performed for real image data, combining the proposed parameter identification and estimation algorithms. The results show that this method gives considerable improvement in SNR.     

Robust and efficient map-to-image registration with line segments

This work investigates map-to-image registration for planar scenes in the context of robust parameter estimation. Registration is posed as the problem of estimating a projective transformation which optimally aligns transformed model line segments from a map with data line segments extracted from an image. Matching and parameter estimation is solved simultaneously by optimizing an objective function which is based on M-estimators, and depends on overlap and the weighted orthogonal distance between transformed model segments and data segments. An extensive series of registration experiments was conducted to test the performance of the proposed parameter estimation algorithm. More than 200 000 registration experiments were run with different objective functions for 12 aerial images and randomly corrupted maps distorted by randomly selected projective transformations. Received: 10 August 2000 / Accepted: 29 January 2001     

Pseudo Bond Graph modelling and on-line estimation of unknown kinetics for a wastewater biodegradation process

This paper deals with the problem of modelling and on-line estimation of kinetics for a biomethanation process. This bioprocess is in fact a wastewater biodegradation process with production of methane gas, which takes place inside a Continuous Stirred Tank Bioreactor. The reaction scheme and the analysis of biochemical phenomena inside the bioreactor are used in order to obtain a nonlinear dynamic model of the bioprocess, by means of the pseudo Bond Graph method. Two nonlinear estimation strategies are developed for the identification of unknown kinetics of the bioprocess. First, an estimator is developed by using a state observer based technique. Second, an observer based on high-gain approach is designed and implemented. Several numerical simulations are performed in order to analyse and compare the behaviour and the performance of the proposed estimators.     

基于HYSYS的催化重整流程模拟及其应用

选取催化重整18集总31反应集总动力学模型,以流程模拟软件HYSYS为工具,建立了催化重整过程稳态模型。将模型参数估计问题转化为优化问题,在MATLAB中使用ActiveX技术调用HYSYS模型,利用Marquardt算法对模型进行参数估计,并利用工业数据对模型进行了验证。基于HYSYS稳态模型对催化重整过程进行了灵敏度分析,得出操作参数、进料性质和产品指标之间的关系,仿真结果与理论分析一致,从而能够对催化重整过程的监控和优化提供指导。     

基于扩展卡尔曼滤波的声传感器跟踪算法

针对声传感器单站单目标跟踪,提出了一种基于扩展卡尔曼滤波(EKF)的跟踪算法,将声波传输时延的影响转换到运动模型的可变周期上,通过参数在线估计的方法,估计该可变周期,进而解决了有信号时延的跟踪问题。通过把先验已知的速率当作观测值,解决了纯方位角跟踪时系统不完全可测的问题。仿真验证了算法的正确性和有效性。     

Numerical solution of a nonlocal identification problem for nonlinear ion transport

A numerical method for the solution of a parameter identification problem in a nonlinear non-self-adjoint two-point boundary value problem with an additional nonlocal condition defining the parameter is presented. The equation arises in the modelling of an experiment known as chronoamperometry for the study of kinetics and mass-transfer in electrochemical events. The algorithm is based on the reformulation of the identification problem as a nonlinear fixed-point problem involving the concentration flux of the reduced species. The linearized boundary value problem is shown to have a unique solution with the unknown parameter uniquely determined by the flux. The linearized BVP is solved using finite differences and the fixed-point is found using the α-bisection method. The results of computational experiments are presented and their physical significance is discussed.     

基于粒子群优化算法的Richards模型参数估计和算法有效性

针对Richards模型参数估计较为困难的实际问题,提出将Richards模型的参数估计问题转化为一个多维无约束函数优化问题。结合谷氨酸菌体的实际生长浓度数据,在Matlab 2012b环境中,利用粒子群优化（PSO）算法建立适应度函数,在最小线性二乘意义下估计Richards模型中的4个参数,并建立了拟合的生长曲线和最优值变化曲线。为进一步验证算法有效性,将PSO算法与该模型传统参数估计法中的四点法和遗传算法(GA)进行了比较,以相关指数和剩余标准差作为评价指标。结果表明,PSO算法对Richards模型的拟合效果良好,对模型的参数估计有着很好的适用性。