Modified Levenberg-Marquardt algorithm for source localization using AOAs in the presence of sensor location errors

In this paper,by utilizing the angle of arrivals(AOAs) and imprecise positions of the sensors,a novel modified Levenberg-Marquardt algorithm to solve the source localization problem is proposed.Conventional source localization algorithms,like Gauss-Newton algorithm and Conjugate gradient algorithm are subjected to the problems of local minima and good initial guess.This paper presents a new optimization technique to find the descent directions to avoid divergence,and a trust region method is introduced to accelerate the convergence rate.Compared with conventional methods,the new algorithm offers increased stability and is more robust,allowing for stronger non-linearity and wider convergence field to be identified.Simulation results demonstrate that the proposed algorithm improves the typical methods in both speed and robustness,and is able to avoid local minima.

Fast Fading Channel Neural Equalization Using Levenberg-Marquardt Training Algorithm and Pulse Shaping Filters

Artificial Neural Network (ANN) equalizers have been successfully applied to mitigate Inter symbolic Interference (ISI) due to distortions introduced by linear or nonlinear communication channels. The ANN architecture is chosen according to the type of ISI produced by fixed, fast or slow fading channels. In this work, we propose a combination of two techniques in order to minimize ISI yield by fast fading channels, i.e., pulse shape filtering and ANN equalizer. Levenberg-Marquardt algorithm is used to update the synaptic weights of an ANN comprise only by two recurrent perceptrons. The proposed system outperformed more complex structures such as those based on Kalman filtering approach.

改进的基于LMA算法的电子稳像技术

提出了一种改进的基于LMA算法的电子稳像技术.通过对LMA进行变换和改进将它运用到仿射变换模型的参数估计当中,从而获得参考帧与当前帧之间的全局运动矢量.通过变换降低了Hessian矩阵的计算量,并将多分辨率的匹配技术引入到算法当中,使得该电子稳像技术具有速度快和精度高的特点.实验表明,该技术对于视频的稳像具有良好的效果.

一种基于LMA的电子稳像技术

提出一种基于LMA的电子稳像技术,将LMA运用到仿射变换模型的参数估计中,从而获得参考帧与当前帧之间的运动矢量。该电子稳像方法具有速度快和精度高的特点。实验表明,这种稳像算法在进行视频稳像时具有很好的效果。

Modified imperialist competitive algorithm-based neural network to determine shear strength of concrete beams reinforced with FRP

Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest;however, their use is hindered because their brittle shear is formulated in most specifications using limited data available at the time. We aimed to predict the shear strength of concrete beams reinforced with FRP bars and without stirrups by compiling a relatively large database of 198 previously published test results (available in appendix). To model shear strength, an artificial neural network was trained by an ensemble of Levenberg-Marquardt and imperialist competitive algorithms. The results suggested superior accuracy of model compared to equations available in specifications and literature.

Fuzzy optimization neural network model based on LM algorithm

A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In this new model,the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training,which changes the weights adjusting equations of the network and increases the training speed. Moreover,to avoid the results yielding to local minimum,the transfer function is also revised to sigmoid function. A case study is utilized to validate this new model,and the results reveal that the new model fast training speed and better forecasting capability.

An Algorithm for Cavity Reconstruction in Electrical Impedance Tomography

We consider the inverse problem of finding cavities within some object from electrostatic measurements on the boundary. By a cavity we understand any object with a different electrical conductivity from the background material of the body. We give an algorithm for solving this inverse problem based on the output nonlinear least-square formulation and the regularized Newton-type iteration. In particular, we present a number of numerical results to highlight the potential and the limitations of this method.

Design Optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence Algorithm

The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to the load and generates heat and losses,reducing its energy transfer efficiency.This issue has become an obstacle for PMEC to develop toward a higher power.This paper aims to improve the overall performance of PMEC through multi-objective optimization methods.Firstly,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC.Then,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of PMEC.Chaotic search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the algorithm.In order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization algorithms.Applying them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive performance.Finally,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results.Compared with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency.

几何约束求解的扩展C-树分解法

为了有效分解几何约束系统,本文提出了一种基于增量LMA(ILMA)算法的扩展C-树分解法,该方法将几何约束系统分解为一棵扩展C-树。与C-树分解法相比,该方法能够保证以任意装配几何约束的方式构造的扩展C-树是几何约束系统的最大化分解。实例分解结果表明:将本文方法应用于几何约束求解是行之有效的。

改进的BP神经网络在故障诊断中的应用

介绍了BP神经网络的结构和学习方法,针对BP网络存在局部极小值和收敛速度慢等问题．提出采用一种基于Levenbe rg-Marquardt算法(LM算法)的改进BP网络,最后通过汽轮机故障诊断实例的仿真研究,结果表明改进算法的性能明显优于常规BP算法,经改进算法训练的网络能大大提高故障的在线诊断能力．具有广泛的应用前景和应用价值。

摄像机运动下基于背景匹配的运动目标检测

提出一种摄像机运动下的运动目标检测技术。首先获取相邻两帧图像上的对应特征点的坐标后应用到仿射变换模型得到全局运动参量来描述背景图像的运动变化。然后根据估计出的全局运动参量通过双线性内插法完成背景匹配。最后采用背景匹配后的两帧图像差进行目标检测。实验证明本算法在提高处理的精确度和时效性的同时还可以处理摄像机发生较大幅度旋转运动情况下的目标检测。

Prediction of Properties in Thermomechanically Treated Cu-Cr-Zr Alloy by an Artificial Neural Network

A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of thermomechanical treatment processes with respect to hardness and conductivity properties was proposed for Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of thermomechanical treatment processes is established via sufficient data acquisition by the network. The results showed that the ANN system is an effective way and can be successfully used to predict and analyze the properties of Cu-Cr-Zr alloy.

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

Autonomous optical navigation for interplanetary exploration based on information of earth-moon

The image elements of earth-center and moon-center are obtained by processing the images of earthand moon, these image elements in combination with the inertial attitude information and the moon ephemerisare utilized to obtain the probe initial position relative to earth, and the Levenberg-Marquardt algorithm is usedto determine the accurate probe position relative to earth, and the probe orbit relative to earth is estimated by u-sing the extended Kalman filter. The autonomous optical navigation algorithm is validated using the digital simu-lation.

Self-adaptive Green-Ampt infiltration parameters obtained from measured moisture processes

The Green-Ampt(G-A) infiltration model(i.e., the G-A model) is often used to characterize the infiltration process in hydrology. The parameters of the G-A model are critical in applications for the prediction of infiltration and associated rainfall-runoff processes. Previous approaches to determining the G-A parameters have depended on pedotransfer functions(PTFs) or estimates from experimental results, usually without providing optimum values. In this study, rainfall simulators with soil moisture measurements were used to generate rainfall in various experimental plots. Observed runoff data and soil moisture dynamic data were jointly used to yield the infiltration processes, and an improved self-adaptive method was used to optimize the G-A parameters for various types of soil under different rainfall conditions. The two G-A parameters, i.e., the effective hydraulic conductivity and the effective capillary drive at the wetting front, were determined simultaneously to describe the relationships between rainfall, runoff, and infiltration processes. Through a designed experiment, the method for determining the GA parameters was proved to be reliable in reflecting the effects of pedologic background in G-A type infiltration cases and deriving the optimum G-A parameters. Unlike PTF methods, this approach estimates the G-A parameters directly from infiltration curves obtained from rainfall simulation experiments so that it can be used to determine site-specific parameters. This study provides a self-adaptive method of optimizing the G-A parameters through designed field experiments. The parameters derived from field-measured rainfall-infiltration processes are more reliable and applicable to hydrological models.

NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R^2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.

Method of Fire Image Identification Based on Optimization Theory

In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.

An improved potential field method for mobile robot navigation

In order to overcome the inherent oscillation problem of potential field methods(PFMs) for autonomous mobile robots in the presence of obstacles and in narrow passages,an enhanced potential field method that integrates Levenberg-Marquardt(L-M) algorithm and k-trajectory algorithm into the basic PFMs is proposed and simulated.At first,the mobile robot navigation function based on the basic PFMs is established by choosing Gaussian model.Then,the oscillation problem of the navigation function is investigated when a mobile robot nears obstacles and passes through a long and narrow passage,which can cause large computation cost and system instability.At last,the L-M algorithm is adopted to modify the search direction of the navigation function for alleviating the oscillation,while the k-trajectory algorithm is applied to further smooth trajectories.By a series of comparative experiments,the use of the L-M algorithm and k-trajectory algorithm can greatly improve the system performance with the advantages of reducing task completion time and achieving smooth trajectories.

Thermophysical properties of pure gases and mixtures at temperatures of 300-30000K and atmospheric pressure:thermodynamic properties and solution of equilibrium compositions

The equilibrium compositions and thermodynamic properties(density,enthalpy,etc at constant pressure)of plasma of pure gases and mixtures under local thermodynamic nonequilibrium have been calculated in this paper.The homotopy Levenberg-Marquardt algorithm was proposed to accurately solve nonlinear equations with singular Jacobian matrices,and is constructed by the Saha equation and Guldberg-Waage equation combined with mass conservation,the electric neutrality principle and Dalton’s partial pressure law,to solve the problem of dependence on the initial value in the process of iteration calculation.In this research,the equations at a higher temperature were solved and used as the auxiliary equations,and the homotopy control parameters’sequence of the homotopy equations was selected by equal ratios.For auxiliary equations,the iterative initial value was obtained by assuming that there were only the highestvalence atomic cations and electrons at this temperature,and the plasma equilibrium composition distribution with the required accuracy was ultimately solved under the current conditions employing the Levenberg-Marquardt algorithm.The control parameter sequence was arranged according to the geometric sequence and the homotopy step was gradually shortened to ensure continuity of the homotopy process.Finally,the equilibrium composition and thermodynamic properties of pure N_(2),Mg(30%)-CO_(2)(70%)and Mg(40%)-CO(50%)-N_(2)(10%)mixture plasma at atmospheric pressure were calculated and the calculation process of some specified temperatures was shown and analyzed.The calculation accuracy of equilibrium composition is higher than other findings in the literature.The results for the thermodynamic properties are in good agreement with data reported by the literature.