Intelligent methods for solving inverse problems of backscattering spectra with noise: a comparison between neural networks and simulated annealing

This paper investigates two different intelligent techniques—the neural network (NN) method and the simulated annealing (SA) algorithm for solving the inverse problem of Rutherford backscattering (RBS) with noisy data. The RBS inverse problem is to determine the sample structure information from measured spectra, which can be defined as either a function approximation or a non-linear optimization problem. Early studies emphasized on numerical methods and empirical fitting. In this work, we have applied intelligent techniques and compared their performance and effectiveness for spectral data analysis by solving the inverse problem. Since each RBS spectrum may contain up to 512 data points, principal component analysis is used to make the feature extraction so as to ease the complexity of constructing the network. The innovative aspects of our work include introducing dimensionality reduction and noise modeling. Experiments on RBS spectra from SiGe thin films on a silicon substrate show that the SA is more accurate but the NN is faster, though both methods produce satisfactory results. Both methods are resilient to 10% Poisson noise in the input. These new findings indicate that in RBS data analysis the NN approach should be preferred when fast processing is required; whereas the SA method becomes the first choice should the analysis accuracy be targeted.     

Modelling of a magneto-rheological damper by evolving radial basis function networks

This paper presents an approach to approximate the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper using evolving radial basis function (RBF) networks. Due to the highly nonlinear characteristics of MR dampers, modelling of MR dampers becomes a very important problem to their applications. In this paper, an alternative representation of the MR damper in terms of evolving RBF networks, which have a structure of four input neurons and one output neuron to emulate the forward and inverse dynamic behaviours of an MR damper, respectively, is developed by combining the genetic algorithms (GAs) to search for the network centres with other standard learning algorithms. Training and validating of the evolving RBF network models are achieved by using the data generated from the numerical simulation of the nonlinear differential equations proposed for the MR damper. It is shown by the validation tests that the evolving RBF networks can represent both forward and inverse dynamic behaviours of the MR damper satisfactorily.     

On Solving the Inverse Scattering Problem with RBF Neural Networks: Noise-Free Case

Neural networks are successfully used to determine small particle properties from knowledge of the scattered light – an inverse light scattering problem. This type of problem is inherently difficult to solve as it is represented by a highly ill-posed function mapping. This paper presents a technique that solves the inverse light scattering problem for spheres using Radial Basis Function (RBF) neural networks. A two-stage network architecture is arranged to enhance network approximation capability. In addition, a new approach to computing basis function parameters with respect to the inverse scattering problem is demonstrated. The technique is evaluated for noise-free data through simulations, in which a minimum 99.06% approximation accuracy is achieved. A comparison is made between the least square and the orthogonal least square training methods.     

Nonlinear blind source separation using a radial basis functionnetwork

This paper proposes a novel neural-network approach to blind source separation in nonlinear mixture. The approach utilizes a radial basis function (RBF) neural-network to approximate the inverse of the nonlinear mixing mapping which is assumed to exist and able to be approximated using an RBF network. A contrast function which consists of the mutual information and partial moments of the outputs of the separation system, is defined to separate the nonlinear mixture. The minimization of the contrast function results in the independence of the outputs with desirable moments such that the original sources are separated properly. Two learning algorithms for the parametric RBF network are developed by using the stochastic gradient descent method and an unsupervised clustering method. By virtue of the RBF neural network, this proposed approach takes advantage of high learning convergence rate of weights in the hidden layer and output layer, natural unsupervised learning characteristics, modular structure, and universal approximation capability. Simulation results are presented to demonstrate the feasibility, robustness, and computability of the proposed method.     

VBA在处理无线基站信息数据中的应用

在实际的电信规划设计工程实践中,经常需要用Excel处理数量庞大的无线基站信息数据,单纯依靠Excel自带的工作表函数往往不能完成数据处理的要求,而利用VBA(VisualbasicforApplication)编写宏却能实现对无线基站信息数据快速高效的处理。本文通过具体实例详细介绍了在Excel2000中用VBA编写宏处理无线基站信息数据的一种应用方法,实践证明应用此法可以大大提高无线基站信息处理的效率。     

一种新的基于构造型RBF神经元网络分类算法

依据RBF神经元模型的几何解释,提出一种新的构造型神经网络分类算法.首先从样本数据本身入手,通过引入一个密度估计函数来对样本数据进行聚类分析;然后在特征空间里构造超球面,以逼近样本点分布的几何轮廓,从而将神经网络训练问题转化为点集＂包含＂问题.该算法有效克服了传统神经网络训练时间长、学习复杂的缺陷,同时也考虑了神经网络规模的优化问题.实验证明了该算法的有效性.     

基于RBF和优化Wiener模型的轴承剩余寿命预测

针对滚动轴承寿命准确预测缺乏表征其健康状态的可靠退化指标的问题,提出径向基(RBF)神经网络及带有漂移参数的维纳(Wiener)模型进行剩余寿命预测.首先,使用小波包奇异谱熵提取轴承振动信号初始特征;其次,利用早期无故障样本特征和失效样本特征训练RBF神经网络模型,将已提取特征全寿命数据输入到RBF神经网络模型,计算隶...     

基于高亮特征匹配的双视向SAR图像建筑物高度提取

由于城区场景的复杂性和SAR成像几何畸变的影响,基于单幅SAR图像的建筑物高度提取常常存在很大困难。针对这一问题,利用建筑物目标SAR成像形成的叠掩、二次散射、较强单次散射等散射机制对应的高亮特征非常典型,并且对方向性敏感的特点,提出了一种基于双视向SAR图像高亮特征与几何模型匹配的建筑物高度提取方法。首先分析了建筑物目标的SAR图像散射特征及对雷达视向的敏感性,然后构造了建筑物目标在双视向SAR图像上高亮特征几何模型,然后基于灰度均值、灰度概率分布、边界信息定义匹配函数,并利用多种群遗传算法进行优化求解,最终得到建筑物目标的高度信息。基于模拟和机载SAR图像的试验表明该方法的建筑物高度平均反演误差小于1m,可以有效提高建筑物高度反演的精度。     

基于PCA和神经网络的农残含量预测模型研究

为提高快速检测农残含量的精度,针对建模数据特征发生明显变化的实际情况,提出了一种结合主成分分析（PCA）和神经网络的分段多模型方法。提取建模数据的前2个主成分作为模型的输入,分别使用主成分回归（PCR）和BP/RBF神经网络建立单一及分段多模型。通过计算模型验证集的输出总误差和误差百分比,对比模型检测精度。试验表明：与单一模型相比,利用神经网络建立的分段多模型可以显著降低农药含量的预测误差,使用BP和RBF网络建立的低浓度段模型的输出误差百分比分别为0.8%和0.4%,RBF网络效果更好。该方法可以在待测农药的较大浓度范围内实现定量检测,具有较强的实用性。     

Locally regularised two-stage learning algorithm for RBF network centre selection

Nonlinear system models constructed from radial basis function (RBF) networks can easily be over-fitted due to the noise on the data. While information criteria, such as the final prediction error (FPE), can provide a trade-off between training error and network complexity, the tunable parameters that penalise a large size of network model are hard to determine and are usually application dependent. This article introduces a new locally regularised, two-stage stepwise construction algorithm for RBF networks. The main objective is to produce a parsimonious network that generalises well over unseen data. This is achieved by utilising Bayesian learning within a two-stage stepwise construction procedure to penalise centres that are mainly interpreted by the noise. Specifically, each output layer weight is assigned a hyperparameter, a large value of such a parameter forcing the associated output layer weight to be near to zero. Sparsity is achieved by removing irrelevant RBF centres from the network. The efficacy of proposed algorithm from the original two-stage construction method is retained. Numerical analysis shows that this new method only needs about half of the computation involved in the locally regularised orthogonal least squares (LROLS) alternative. Results from two simulation examples are presented to show that the nonlinear system models resulting from this new approach are superior in terms of both sparsity and generalisation capability.