基于粒子群优化的三轴磁强计非线性误差校正

三轴磁强计的非线性误差是影响其测量精度的重要因素,而传统三轴磁强计误差模型仅考虑零偏、磁轴非正交和灵敏度误差,无法实现对磁测误差有效剥离和校正.通过对传统误差模型进行扩展,提出了三轴磁强计的非线性误差模型,并利用自适应粒子群优化(APSO)算法对非线性误差模型参数进行反演.实验结果表明:可以有效地补偿三轴磁强计的非线性测量误差,相较于传统校正方法,在误差参数规模较大情况下,APSO算法具有更好的全局搜索能力,可大幅提高误差参数反演精度及算法收敛速度.     

Genetic algorithm based NARX model identification for evaluation of insulin sensitivity

The evaluation of insulin sensitivity plays an important role in the clinical investigation of glucose related diseases. Mathematical models based on non-invasive tests provide an estimate of insulin sensitivity by solving a nonlinear optimization problem. However traditional optimization methods suffer from convergence problem and the final estimate is heavily dependent on the initial parameterization. This paper proposes a model based on the hybrid approach of nonlinear autoregressive with exogenous input (NARX) modeling and genetic algorithm (GA) for deriving an index of insulin sensitivity. The model does not need an initial parameterization and the convergence is always guaranteed. The index derived from the proposed model is found to correlate well with the widely used minimal model based insulin sensitivity, with a significantly higher accuracy of fit.     

A hybrid genetic algorithm for the single machine scheduling problem with sequence-dependent setup times

This paper presents a hybrid approach based on the integration between a genetic algorithm (GA) and concepts from constraint programming, multi-objective evolutionary algorithms and ant colony optimization for solving a scheduling problem. The main contributions are the integration of these concepts in a GA crossover operator. The proposed methodology is applied to a single machine scheduling problem with sequence-dependent setup times for the objective of minimizing the total tardiness. A sensitivity analysis of the hybrid approach is carried out to compare the performance of the GA and the hybrid genetic algorithm (HGA) approaches on different benchmarks from the literature. The numerical experiments demonstrate the HGA efficiency and effectiveness which generates solutions that approach those of the known reference sets and improves several lower bounds.     

A hybrid approach to the single line scheduling problem with multiple products and sequence-dependent time

The present paper presents a hybrid approach for solving manufacturing scheduling problems, based on the integration between Constraint Logic Programming (CLP) and Genetic Algorithm (GA) approaches. The proposed methodology is applied to a single line with multiple products and sequence-dependent time. This system model derives from a real case of a company producing sheets for catalytic converters. A sensitivity analysis of the hybrid methodology is carried out to compare the performance of the CLP, GA and integrated CLP–GA approaches.     

Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks

This paper investigates the development and experimental implementation of an adaptive dynamic nonlinear model inversion control law for a Twin Rotor MIMO System (TRMS) using artificial neural networks. The TRMS is a highly nonlinear aerodynamic test rig with complex cross-coupled dynamics and therefore represents the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. The proposed on-line learning algorithm updates the weights and biases of the neural network using the error between the set-point and the real output. The real-time response of the method shows a satisfactory tracking performance in the presence of inversion errors caused by model uncertainty. The approach is therefore deemed to be suitable to apply real-time to other nonlinear systems with necessary modifications.     

Stable inverse control for nonminimum-phase nonlinear processes

Since most of the chemical processes happen to be nonlinear, nonlinear controllers with feedback linearization have been recently designed which can directly cancel the nonlinearities and establish a linear input/output map. However, this I/O linearization algorithm requires the control with stable inversion and state feedback. In the present study, a stable inversion technique for nonminimum-phase systems is developed under appropriate assumptions, which includes several steps such as finding the bounded control and bounded state trajectories for tracking the desired signal. In this design, the output tracking error as well as the effect of measurable disturbances can be asymptotically eliminated through generating a feedforward control. Finally, the proposed control methodology is applied to a chemical process, viz. a Van de Vusse reaction in an isothermal or adiabatic stirred-tank reactor, and its performance is evaluated.     

Intelligent computing for numerical treatment of nonlinear prey–predator models

In this study, a new computing paradigm is presented for evaluation of dynamics of nonlinear prey–predator mathematical model by exploiting the strengths of integrated intelligent mechanism through artificial neural networks, genetic algorithms and interior-point algorithm. In the scheme, artificial neural network based differential equation models of the system are constructed and optimization of the networks is performed with effective global search ability of genetic algorithm and its hybridization with interior-point algorithm for rapid local search. The proposed technique is applied to variants of nonlinear prey–predator models by taking different rating factors and comparison with Adams numerical solver certify the correctness for each scenario. The statistical studies have been conducted to authenticate the accuracy and convergence of the design methodology in terms of mean absolute error, root mean squared error and Nash-Sutcliffe efficiency performance indices.     

An example of robust internal model control under variable and uncertain delay

This paper proposes a particular study of the classic internal model control algorithm for a sampled-data system in a generalized context of uncertainty. Besides the usually considered model mismatch, the particularity of the case under consideration is that the measurements available to the control algorithm suffer from large, varying and uncertain delays. The presented study considers a simple SISO nonlinear system. The control algorithm is a sampled nonlinear model-based controller with successive model inversion and bias correction. The main contribution of this article is its proof of global convergence and robustness despite time-varying delays and uncertain measurement dating. In particular, the model error, the varying delays and measurements dating error are treated using monotonicity of the system and a detailed analysis of the closed-loop behaviour of the sampled dynamics, in an appropriate norm.     

一种新型智能化的入侵检测系统

提出了融合三种机器学习技术的新型入侵检测系统,其中包括模糊综合评判、遗传算法、BP神经网络。首先围绕入侵检测的评估标准简述了三种技术引入的原因,其次重点介绍了如何在入侵检测系统中实现三种机器学习的融合,包括用模糊综合评判来建立基础模型,以及用遗传算法优化隶属函数参数,并利用BP神经网络对入侵检测系统进行学习和判断,从而提高了入侵检测的快速性、准确性以及自适应性。     

基于遗传优化神经网络的网络入侵特征检测

为了提高网络入侵检测正确率,提出一种遗传优化神经网络的网络入侵特征选择和检测算法。该方法先将网络状态特征和RBF神经网络参数作为遗传算法的个体,把检测正确率作为适应度函数;然后利用遗传算法的选择、交叉和变异等操作对网络状态特征和RBF神经网络参数进行优化,最后利用KDD 1999数据集对算法性能进行测试。测试结果表明：遗传优化神经网络能够快速获得最优网络状态特征和分类器参数,同时提高了网络入侵检测正确率。     

Landmark Matching via Large Deformation Diffeomorphisms on the Sphere

This paper presents a methodology and algorithm for generating diffeomorphisms of the sphere onto itself, given the displacements of a finite set of template landmarks. Deformation maps are constructed by integration of velocity fields that minimize a quadratic smoothness energy under the specified landmark constraints. We present additional formulations of this problem which incorporate a given error variance in the positions of the landmarks. Finally, some experimental results are presented. This work has application in brain mapping, where surface data is typically mapped to the sphere as a common coordinate system.     

基于参数灵敏度分析的交通仿真模型校正研究

为了使交通仿真模型校正工作能够高效开展,提出了以参数灵敏度分析为基础的模型校正框架。通过灵敏度分析确定影响模型精度的关键参数,以简化模型;对关键参数进行标定,以校正模型。以城市快速路交织区为仿真案例,以跟车模型和换道模型为研究对象;首先进行了大量仿真实验,分析不同车流量水平下模型参数的取值特征;据此制定模型参数的区间划分规则和交叉组合规则,从而对LH-OAT算法和遗传算法（GA）进行改进;然后应用改进LH-OAT算法（ILH-OAT）对模型参数进行灵敏度分析,再应用GA对关键参数进行标定;最后依据校验指标对仿真结果进行误差分析。结果表明ILH-OAT和GA相结合,不仅简化了仿真模型,降低了仿真运行成本,仿真效果也更加接近真实的道路交通运行情况。     

Numerical inversion of z-transforms with application to polymerization kinetics

A numerical technique for determination of the polymer chain length distribution from mathematical models of polymerization systems is presented. The proposed method, which can be applied to the class of model equations whose z-transform exists, is based upon a z-transform inversion formula that has the form of an Inverse Discrete Fourier Transform. A computer algorithm that employs the FFT to efficiently obtain numerical results from the derived formulas is summarized and an error analysis is given. An example problem that describes the polycondensation polymerization kinetics of bisphenol-A to polycarbonate is introduced as a realistic application of the proposed method. It is shown that for this example, both the accuracy and computational efficiency of the proposed z-transform inversion technique are superior to another independent method for obtaining the polymer chain length distribution that is based upon direct numerical integration. A methodology for identification of polymerization kinetic rate parameters from experimental polymer molecular weight distributions is also suggested.     

Mixed formulation of nonlinear beam on foundation elements

This paper presents an inelastic element for the analysis of beams on foundations. The element is derived from a two-field mixed formulation with independent approximation of forces and displacements. The state determination algorithm for the implementation of the element in a general purpose nonlinear finite element analysis program is presented and its stability characteristics are discussed. Numerical studies are performed to compare the model with the classical displacement formulation. The studies confirm the superiority of the proposed model in describing the inelastic behavior of beams on foundations.     

Genetic adaptive state estimation

A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for parallel search of complex spaces. This paper describes a GA that can perform on-line adaptive state estimation for linear and nonlinear systems. First, it shows how to construct a genetic adaptive state estimator where a GA evolves the model in a state estimator in real time so that the state estimation error is driven to zero. Next, several examples are used to illustrate the operation and performance of the genetic adaptive state estimator. Its performance is compared to that of the conventional adaptive Luenberger observer for two linear system examples. Next, a genetic adaptive state estimator is used to predict when surge and stall occur in a nonlinear jet engine. Our main conclusion is that the genetic adaptive state estimator has the potential to offer higher performance estimators for nonlinear systems over current methods.     

基于逆模型辨识的Wiener型传感器动态补偿研究

在测量系统中许多传感器动态特性是一个非线性Wiener模型,即存在着严重的静态非线性和动态响应滞后.为了补偿动态误差,采用模型参考和Wiener逆模型辨识的算法建立动态补偿单元.补偿单元由一个静态逆模型和动态逆模型构成.通过静态标定方法,采用单输入/单输出的模糊小脑神经网络(SISO-FCMAC)建立传感器静态非线性模...     

Output error convergence of adaptive filters with compensation foroutput nonlinearities

Output error convergence of a Wiener model-based nonlinear stochastic gradient algorithm is analyzed. The normalized scheme estimates the parameters of a linear finite impulse response model in cascade with a known output nonlinearity. The algorithm can be interpreted as a normalized least mean square algorithm with compensation for an output nonlinearity. Linearizing inversion of the nonlinearity is not utilized. Global output error convergence is then proved, provided that the nonlinearity is monotone (not strictly monotone), and provided that a previously observed mechanism resulting in deadlock does not occur. The algorithm and the analysis include important practical cases like sensor saturation and dead zones that must be excluded when global parametric convergence is studied     

Efficient radiative transfer model inversion for remote sensing applications

A simple method for efficient inversion of arbitrary radiative transfer models for image analysis is presented. The method operates by representing the shape of the function that maps model parameters to spectral reflectance by an adaptive look-up tree (ALUT) that evenly distributes the discretization error of tabulated reflectances in spectral space. A post-processing step organizes the data into a binary space partitioning tree that facilitates an efficient inversion search algorithm. In an example shallow water remote sensing application, the method performs faster than an implementation of previously published methodology and has the same accuracy in bathymetric retrievals. The method has no user configuration parameters requiring expert knowledge and minimizes the number of forward model runs required, making it highly suitable for routine operational implementation of image analysis methods. For the research community, straightforward and robust inversion allows research to focus on improving the radiative transfer models themselves without the added complication of devising an inversion strategy.     

程序调试中的树形结构演变可视化模型

树形结构作为一种非线性数据结构,在程序执行过程中的演变过程较为抽象,尤其是在对其进行加工型操作时,容易发生内存泄漏。针对编程初学者难以掌握树形结构的逻辑演变过程,以及在程序中发生内存泄漏错误时调试较为困难的问题,文中提出了一种对程序调试过程中树形结构的演变过程进行可视化呈现的处理模型TEVM(Tree Evaluation Visualization Model)。针对单个可视化程序在调试步骤前和调试步骤后的两个树形结构,设计了一种将树形结构转换为线性表示的结构对比算法,得出了它们的包括泄漏树在内的结构差异;同时设计了一种树形结构布局方法,并计算它们的布局差异。根据结构差异和布局差异生成可视化演变序列,调用绘图引擎对该序列进行解析和执行,从而完成对树形结构及其演变过程的动态、平滑和直观的可视化呈现,帮助编程初学者快速理解树形结构相关程序的执行过程,提升调试效率。将TEVM模型集成于一个面向编程实训教学的集成开发环境原型Web AnyviewC中,取得了较好的应用效果。     

遗传算法在圆度误差评定中的应用

针对传统最小二乘圆优化算法评定圆度误差局部收敛的问题,提出基于遗传算法的圆度误差评定方法。介绍了遗传算法基本原理和运算流程,在建立最小二乘圆数学模型的基础上,推导出待优化的目标函数,并详细描述了基于遗传算法的圆度评定优化步骤。实例计算结果表明该算法能比传统优化算法收敛到更精确的解,并具有较高的稳定性,能有效地克服局部收敛的问题。