EMD结合RBF神经网络新混合模型及股指期货价格预测

中国近4年才成立的股指期货市场价格呈现出非平稳、非线性的信号特征,传统的预测方法无法对长相关序列进行精确预测.将EMD与RBF相结合,建立了一种新的预测方法对我国股指期货日结算价格进行预测.结果显示本模型将原本具有长相关性质的原始序列分解为若干个短相关性质的不同频带,解决了原始序列随机性强,以及因相邻频带的干扰而造成的系统动力信息反映不足的缺陷;并与其他预测模型进行比较,显示出较高的预测精度.     

基于RBF网络的断路器电流自适应保护研究

针对传统断路器电流保护方法存在受系统运行方式影响、整定困难、智能化低等问题,本文提出了基于RBF的断路器电流自适应保护算法,并给出了算法的模型。该算法融合了RBF神经网络的故障检测和电流自适应保护。首先通过RBF网络检测负载线路的电流故障,然后用电流自适应算法进行保护。在对神经网络进行训练时,利用PSO算法对RBF神经网络的参数进行优化以此来提高网络的泛化能力和学习能力;然后采用优化后的PSO-RBF神经网络对电流故障进行诊断。实验表明,该算法较大地提高了断路器智能化管理水平。     

The closed-form particular solutions for Laplace and biharmonic operators using a Gaussian function

Particular solutions play a critical role in solving inhomogeneous problems using boundary methods such as boundary element methods or boundary meshless methods. In this short article, we derive the closed-form particular solutions for the Laplace and biharmonic operators using the Gaussian radial basis function. The derived particular solutions are implemented numerically to solve boundary value problems using the method of particular solutions and the localized method of approximate particular solutions. Two examples in 2D and 3D are given to show the effectiveness of the derived particular solutions.     

一种关于汽车整车不确定需求的组合预测模型

提出了一种针对汽车生产企业整车不确定需求的组合预测模型,即利用神经网络拟合数据基本变化趋势,然后对企业产品的市场占有率进行评价,以调节需求变化趋势.应用实例,对模型进行了验证,说明该模型能够较好地诠释企业产品需求信息,具有较好的预测能力.     

径向基函数神经网络在股票走势模式分类中的应用

探讨了径向基函数神经网络在个股走势模式分类中的应用问题。提出了一种可调基宽度的计算方法和若干数据预处理的措施。实例计算表明，该方法取得了较好的效果     

基于RBF网络的光纤荧光海水叶绿素a含量在线监测系统的研究

介绍了能够实现对海水中叶绿素a含量在线监测的光纤荧光系统。系统将荧光技术、光纤技术和基于RBF(Radial Basis Function Neural Network)神经网络的标定方法相结合，建立了用于海水叶绿素a含量在线监测的最佳RBF网络结构，系统具有结构简单，探头无源及高灵敏度等特点。     

基于径向基函数神经网络的未知模型混沌系统控制

基于径向基函数神经网络的智能方法对混沌进行控制-该方法不需要被控混沌系统的解析模型，控制的目标可以为周期轨道，也可以为连续变化的目标函数，在模型参数发生摄动和存在测量噪声情况下，控制仍然有效-研究了神经网络误差对控制精度的影响，并给出相关的定理及证明-针对Logistic映射和Henon吸引子的仿真结果，表明了此方法的有效性和可行性- 关键词： 混沌控制 径向基函数神经网络 参数摄动 测量噪声     

Environmental noise classifier using a new set of feature parameters based on pitch range

Automatic Noise Recognition was performed in two stages: (1) feature extraction based on the pitch range, found by analyzing the autocorrelation function and (2) classification using a classifier trained on the extracted features. Since most environmental noise types change their acoustical characteristics over time, we focused on the “pitch range” of the sounds in order to extract features. Two different classifiers, Support Vector Machines (SVM) and k-means clustering, were performed and compared using the proposed features. The SVM and k-means clustering classifiers achieve recognition rates up to 95.4% and 92.8%, respectively. Although both classifiers provided high accuracy, the SVM-based classifier outperformed the k-means clustering classifier by approximately 7.4%.     

Palmprint recognition based on the representation in the feature space

In this paper, we propose a palmprint recognition method based on the representation in the feature space. The proposed method seeks to represent the test sample as a linear combination of all the training samples in the feature space and then exploits the obtained linear combination to perform palmprint recognition. We can implement the mapping from the original space to the feature space by using the kernel functions such as radial basis function (RBF). In this method, the selection of the parameter of the kernel function is important. We propose an automatic algorithm for selecting the parameter. The basic idea of the algorithm is to optimize the feature space such that the samples from the same class are well clustered while the samples from different classes are pushed far away. The proposed criterion measures the goodness of a feature space, and the optimal kernel parameter is obtained by minimizing this criterion. Experimental results on multispectral palmprint database show that the proposed method is more effective than 2DPCA, 2DLDA, AANNC, CRC_RLS, nearest neighbor method (NN) and competitive coding method in terms of the correct recognition rate.