基于XRF与RBF神经网络的锌浸出渣有价组分精准定量分析研究

锌冶炼浸出渣是湿法炼锌工艺产出的冶炼固废渣,占锌冶炼固废产出总量的75%以上,因含有Zn,Cu,Pb,Ag,Cd和As等多种有价金属元素,其资源化利用潜力巨大。然而由于其成分含量不稳定,检测精度不足等原因,导致关键元素的资源转化效率难以保证,因此对浸出渣关键资源组分的精准定量分析在锌冶炼行业绿色发展方面具有重大意义。该研究以Zn,Cu,Pb,Cd和As五种目标元素为分析对象,分别采用XRF工作曲线法和XRF结合RBF神经网络模型的方法对浸出渣目标元素定量分析,以相对误差、相对标准偏差作为两种方法的评价指标,对两种方法进行分析比较。首先采用标准添加法对工业现场采集的锌浸出渣配制浓度梯度样,并以此为标准化样品进行ICP-OES检测,随后将ICP-OES检测结果作为目标元素定量分析基准值,对浓度梯度样品进行X射线荧光光谱（XRF）检测,建立目标元素工作曲线,利用工作曲线对各目标元素进行定量分析。同时用XRF光谱数据构建输入矩阵、样品目标元素浓度构建输出矩阵,训练RBF神经网络来构建浸出渣中目标元素多元定标模型,并用此模型实现浸出渣样品目标元素预测。工作曲线法定量分析结果与ICP-OES基准值对比得到相对误差均值为8.5%,标准偏差均值为4.0%;RBF神经网络预测结果与ICP-OES基准值对比得到相对误差均值为0.18%,标准偏差均值为0.58%。结果表明,两种方法均能实现浸出渣样品目标元素的定量分析,但XRF结合RBF神经网络的方法能够对浸出渣样品进行精准定量分析和基体校正,分析结果准确性和精密度优于传统工作曲线分析方法。     

Detection of Cholangiocarcinoma with Fourier Transform Infrared Spectroscopy and Radial Basis Function Neural Network Classification

The aim of this study was to explore the possibility of applying Fourier transform infrared(FTIR) spectroscopy as a medical diagnostic tool based on a neural network classifier for detecting and classifying cholangiocarcinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position(P), intensity(I) and full width at half-maximum(FWHM), were measured, calculated and subsequently compared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function(RBF) network model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between cancerous and normal tissue, including I₁₆₄₀, I₁₅₅₀, I₁₄₆₀, I₁₄₀₀, I₁₂₅₀, I₁₁₂₀, I₁₀₈₀, I₁₀₄₀ and P₁₀₄₀. In the RBF training classification, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.     

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.     

扰动情况下基于RBF网络的混沌系统同步

基于RBF网络,在扰动存在的情况下,对混沌系统的同步和异结构同步问题进行了研究.研究表明本方法能有效地克服干扰对同步所造成的破坏,实现良好的同步效果.最后,利用Lorenz系统进行了仿真.仿真结果证明了所给方法的有效性. 关键词： 混沌系统 同步 RBF神经网络     

基于前向线性预测算法的光纤陀螺零漂的神经网络建模

在详细分析光纤陀螺零漂的基础上,提出了先用滤波算法对光纤陀螺信号进行预处理,然后采用RBF神经网络对滤波后的信号进行建模的方法。针对光纤陀螺信号特点分别采用FLP算法、小波滤波算法、解相关变步长LMS自适应滤波算法对其进行了预处理,比较三种滤波方法,小波滤波算法效果优于其它两种预处理方法,但针对基于预处理后的陀螺信号采用RBF神经网络进行建模时,小波滤波预处理后的信号在建模精度上却是最差的,而对FLP算法滤波后的信号进行RBF建模,建模精度提高了两个数量级。结果表明:基于FLP算法的RBF神经网络在光纤陀螺中的建模是有效的,可大大提高建模的精度。     

基于RBF网络的有源噪声控制

提出一种神经自适应噪声有源控制（ANC）的方法。应用RBF（Radial Basis Function)网络对噪声进行有源控制。针对RBF的网络特点,使用递阶遗传算法确定网络参数（连接权、隐节点中心和宽度），同时解决了网络拓扑结构的优化训练。利用RBF网络的有源噪声控制系统用于三维空间传播的宽频带空调噪声的降噪获得了良好的效果。     

RBFNN结合自适应边界的机器人手臂轨迹跟踪控制系统设计

针对机器人手臂动态模型中存在动态不确定性问题,提出一种结合径向基函数神经网络(RBFNN)和自适应边界控制的机械臂轨迹跟踪方法。利用RBF神经网络在线学习系统中现有的结构化和非结构化不确定性,近似补偿未知动态部分;利用自适应边界来估计非结构化不确定性上的未知边界和神经网络重建误差;通过加权矩阵产生的李雅普诺夫函数证明了该系统具有渐进稳定性。利用三自由度机械臂进行实验,结果表明,相比其他几种较为先进的控制器,本文设计的控制器具有最优的控制精度。     

Adaptive meshless refinement schemes for RBF-PUM collocation

In this paper we present an adaptive discretization technique for solving elliptic partial differential equations via a collocation radial basis function partition of unity method. In particular, we propose a new adaptive scheme based on the construction of an error indicator and a refinement algorithm, which used together turn out to be ad-hoc strategies within this framework. The performance of the adaptive meshless refinement scheme is assessed by numerical tests.     

RBF‐ENO/WENO schemes with Lax–Wendroff type time discretizations for Hamilton–Jacobi equations

In this research, a class of radial basis functions (RBFs) ENO/WENO schemes with a Lax–Wendroff time discretization procedure, named as RENO/RWENO‐LW, for solving Hamilton–Jacobi (H–J) equations is designed. Particularly the multi‐quadratic RBFs are used. These schemes enhance the local accuracy and convergence by locally optimizing the shape parameters. Comparing with the original WENO with Lax–Wendroff time discretization schemes of Qiu for HJ equations, the new schemes provide more accurate reconstructions and sharper solution profiles near strong discontinuous derivative. Also, the RENO/RWENO‐LW schemes are easy to implement in the existing original ENO/WENO code. Extensive numerical experiments are considered to verify the capability of the new schemes.     

A linear system‐free Gaussian RBF method for the Gross‐Pitaevskii equation on unbounded domains

Gaussian radial basis function (RBF) interpolation methods are theoretically spectrally accurate. However, in applications this accuracy is seldom realized due to the necessity of solving a very poorly conditioned linear system to evaluate the methods. Recently, by using approximate cardinal functions and restricting the method to a uniformly spaced grid (or a smooth mapping thereof), it has been shown that the Gaussian RBF method can be formulated in a matrix free framework that does not involve solving a linear system [ 1 ]. In this work, we differentiate the linear system‐free Gaussian (LSFG) method and use it to solve partial differential equations on unbounded domains that have solutions that decay rapidly and that are negligible at the ends of the grid. As an application, we use the LSFG collocation method to numerically simulate Bose‐Einstein condensates. © 2010 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 28: 389–401, 2012