基于支持向量机的非线性系统辨识

本文针对未知非线性系统,提出了基于支持向量机(SVM)的系统辨识方法,并且通过仿真分析比较了基于SVM与基于RBF神经网络系统辨识及预测结果,仿真结果表明SVM方法比RBF神经网络方法具有更高的预测精度和更好的泛化能力。     

基于支持向量机的直接逆模型辨识

在简单讨论逆模型辨识原理的基础上,利用支持向量机(SVM)对函数逼近的能力,提出了基于支持向量机的直接逆模型辨识方法.分别采用二次核函数以及高斯RBF核函数,利用训练数据对线性和非线性系统进行黑箱辨识.仿真结果表明,基于支持向量机的直接逆模型辨识方法在处理线性和非线性对象时,辨识性能都优于传统的BP神经网络,不仅辨识精度高,辨识速度快,而且泛化能力较强.     

多智能体粒子群优化的SVR 模型预测控制

参数的优化选择对支持向量回归机的预测精度和泛化能力影响显著,鉴于此,提出一种多智能体粒子群算法(MAPSO)寻优其参数的方法,并建立MAPSO支持向量回归模型,用于非线性系统的模型预测控制,推导出最优控制率.采用该算法对非线性系统进行仿真,并与基于粒子群算法、基于遗传算法优化支持向量回归机的模型预测控制方法和RBF神经网络的预测控制方法进行比较,结果表明,所提出的算法具有更好的控制性能,可以有效应用于非线性系统控制中.     

基于模糊支持向量机的语音识别方法

通过计算输入样本的模糊隶属度，探讨了模糊支持向量机（FSVM）的原理，应用其对语音信号进行识别。并和RBF神经网络、支持向量机（SVM）的识别效果进行了比较。在仿真实验中，采用小波分析方法提取语音特征向量，识别结果表明，SVM和FSVM比RBF网络具有较好的泛化性能，训练时间也大大缩减。此外，FSVM比SVM有更强的抵抗噪声的能力。     

光滑CHKS孪生支持向量回归机

针对目前光滑孪生支持向量回归机（smooth twin support vector regression ,STSVR）中采用的Sigmoid光滑函数逼近精度不高,从而导致算法泛化能力不够理想的问题,引入一种具有更强逼近能力的光滑（chen‐harker‐kanzow‐smale ,CHKS）函数,采用CHKS函数逼近孪生支持向量回归机的不可微项,并用 Newton‐Armijo 算法求解相应的模型,提出了光滑 CHKS 孪生支持向量回归机（smooth CHKS twin support vector regression ,SCTSVR）。不仅从理论上证明了SCTSVR具有严格凸,能满足任意阶光滑和全局收敛的性能,而且在人工数据集和UCI数据集上的实验表明了SCTSVR比STSVR具有更好的回归性能。     

基于动态粗糙集约简的选择性支持向量机集成

支持向量机集成是提高支持向量机泛化性能的有效手段,个体支持向量机的泛化能力及其之间的差异性是影响集成性能的关键因素。为了进一步提升支持向量机整体泛化性能,提出利用动态粗糙集的选择性支持向量机集成算法。首先在利用Boosting算法对样本进行扰动基础上,采用遗传算法改进的粗糙集与重采样技术相结合的动态约简算法进行特征扰动,获得稳定、泛化能力较强的属性约简集,继而生成差异性较大的个体学习器;然后利用模糊核聚类根据个体学习器在验证集上的泛化误差来选择最优个体;并用支持向量机算法对最优个体进行非线性集成。通过在UCI数据集进行仿真,结果表明算法能明显提高支持向量机的泛化性能,具有较低的时、空复杂性,是一种高效、稳定的集成方法。     

基于支持向量机的混沌时间序列预测分析

混沌的特性决定了混沌系统很难长期预测,支持向量机有强大的学习能力,根据相空间重构理论用支持向量机建立预测模型对混沌时间序列进行短期预测。预测输出构建混沌吸引子来定性评价预测模型性能,同时与BP神经网络RBF神经网络构建的预测模型比较,计算预测模型的均方根误差定量地评价模型的性能。仿真结果表明,该方法具有较高的预测精度和泛化能力。     

基于小样本SVR的迁移学习及其应用

当前机器学习的技术已经运用到很多工程项目中,但大部分机器学习的算法只有在样本数量充足且运用在单一场景中的时候,才能获得良好的结果。其中,经典的支持向量回归机是一种具有良好泛化能力的回归算法。但若当前场景的样本数量较少时,则得到的回归模型泛化能力较差。针对此问题,以加权ε支持向量回归机为基础,提出了一种小样本数据的迁移学习支持向量回归机算法。该算法以加权ε支持向量回归机为Bagging算法的基学习器,使用与目标任务相关联的源域数据,通过自助采样生成多个子回归模型,采用简单平均法合成一个总回归模型。在UCI数据集和现实数据集——玉米棒与花生粒储藏环节损失数据集上的实验结果表明,该算法较标准ε-SVR算法与改进的RMTL算法在小数据样本上有更好的泛化能力。     

基于支持向量机函数逼近的性能研究

通过仿真分析比较支持向量机与前馈神经网络在非线性函数逼近的结果，得出在小样本下，SVM的样本依赖程度、抗噪声能力和泛化性能都优于前馈神经网络。     

基于SVM的软测量建模

支持向量机(Support Vector Machines)是一种基于统计学习理论的新型学习机，本 文提出用支持向量机建立软测量模型．理论分析和仿真研究表明，该方法学习速度快、跟踪 性能好、泛化能力强、对样本的依赖程度低，比基于RBF神经网络的软测量建模具有更好的 推广能力．     

Support vector machine with adaptive parameters in financial time series forecasting

A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.     

基于最小二乘支持向量机的航煤干点软测量应用研究

传统支持向量机是近几年发展起来的一种基于统计学习理论的学习机器,在非线性函数回归估计方面有许多应用。最小二乘支持向量机用等式约束代替传统支持向量机方法中的不等式约束,利用求解一组线性方程得出对象模型,避免了求解二次规划问题。本文采用最小二乘支持向量机解决了航空煤油干点的在线估计问题,结果表明,最小二乘支持向量机学习速度快、精度高,是一种软测量建模的有效方法。在相同样本条件下,比RBF网络具有较好的模型逼近性和泛化性能,比传统支持向量机可节省大量的计算时间。     

RBF neural network based on q-Gaussian function in function approximation

To enhance the generalization performance of radial basis function (RBF) neural networks, an RBF neural network based on a q-Gaussian function is proposed. A q-Gaussian function is chosen as the radial basis function of the RBF neural network, and a particle swarm optimization algorithm is employed to select the parameters of the network. The non-extensive entropic index q is encoded in the particle and adjusted adaptively in the evolutionary process of population. Simulation results of the function approximation indicate that an RBF neural network based on q-Gaussian function achieves the best generalization performance.     

RBF neural network based on q-Gaussian function in function approximation

To enhance the generalization performance of radial basis function (RBF) neural networks, an RBF neural network based on a q-Gaussian function is proposed. A q-Gaussian function is chosen as the radial basis function of the RBF neural network, and a particle swarm optimization algorithm is employed to select the parameters of the network. The non-extensive entropic index q is encoded in the particle and adjusted adaptively in the evolutionary process of population. Simulation results of the function approximation indicate that an RBF neural network based on q-Gaussian function achieves the best generalization performance.     

基于模糊RBF神经网络的函数逼近

提出了一种模糊RBF网络,将模糊逻辑的知识表达以及推理能力和RBF网络的快速学习和泛化能力结合起来,网络结构参数可按实际问题调整,仿真表明网络具有较快的学习速度和较高的函数逼近精度。     

一种改进的神经网络板形模式识别方法

本文提出了一种改进的神经网络板形模式识别方法,该方法基于支持向量机(SVM)与径向基(RBF)网络的结构等价性,利用SVM的回归确定RBF网络较优的初始参数,解决了传统神经网络模式识别方法存在的网络学习时间长,易陷入局部极小值等问题。此外,由于板形标准模式具有两两互反性,将输入样本与基本模式的模糊距离差作为网络输入,使输入节点减少一半,近一步实现了网络结构的固定化和简单化。实验表明,它提高了板形识别精度和速度,可推广到其他标准模式具有两两互反性的模式识别中。     

Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet

To realize effective modeling and secure accurate prediction abilities of models for power supply for high-field magnet (PSHFM), we develop a comprehensive design methodology of information granule-oriented radial basis function (RBF) neural networks. The proposed network comes with a collection of radial basis functions, which are structurally as well as parametrically optimized with the aid of information granulation and genetic algorithm. The structure of the information granule-oriented RBF neural networks invokes two types of clustering methods such as K-Means and fuzzy C-Means (FCM). The taxonomy of the resulting information granules relates to the format of the activation functions of the receptive fields used in RBF neural networks. The optimization of the network deals with a number of essential parameters as well as the underlying learning mechanisms (e.g., the width of the Gaussian function, the numbers of nodes in the hidden layer, and a fuzzification coefficient used in the FCM method). During the identification process, we are guided by a weighted objective function (performance index) in which a weight factor is introduced to achieve a sound balance between approximation and generalization capabilities of the resulting model. The proposed model is applied to modeling power supply for high-field magnet where the model is developed in the presence of a limited dataset (where the small size of the data is implied by high costs of acquiring data) as well as strong nonlinear characteristics of the underlying phenomenon. The obtained experimental results show that the proposed network exhibits high accuracy and generalization capabilities.     

A fast identification algorithm for box-cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.