基于主成分的遗传神经网络股票指数预测研究

数据预测在金融投资领域占有重要地位,预测中输入变量的选取影响着预测的速度和精度,传统方法选取输入变量主观性较强,预测效果欠佳。将遗传算法与BP网络结合,利用GA的全局搜索优化BP网络的结构参数,有效克服BP算法的局部收敛等问题。使用主成分分析法选取输入变量,并将GA—BP混合建模应用于沪市综合指数预测中。实验结果表明,该方法改善了预测精度,达到了较好的预测效果。     

基于数据挖掘技术的企业财务困境预测建模

研究企业财务困境预测问题,影响财务困境的输入变量参数较多,存在输入维数和冗余信息,造成预测效率低。如何准确选择合理的输入变量参数是提高财务困境预测精度的关键。为了解决财务困境输入变量选择不合理导致预测精度低等难题,提出采用主成分-遗传-支持向量机(PCA-GA-SVM)的企业财务困境组合预测方法。先用主成分分析法(PCA)合理选取企业财务困境的输入变量,然后结合遗传算法(GA)利用训练集的数据对SVM最优参数寻优,得到企业财务困境预测模型,最后采用具体企业财务数据进行仿真。实验结果表明,PCA-GA-SVM的企业财务困境预测方法提高了财务困境的预测精度。     

煤自然发火期的主成分神经网络预测模型

煤自然发火期是衡量煤自燃特性的一个重要参数,也是指导井下防灭火工作的重要参考依据。结合主成分分析与神经网络的优点,提出了基于主成分分析的神经网络煤自然发火期预测模型。采用主成分分析法对原始输入变量进行预处理,选择输入变量的主成分作为神经网络输入,一方面减少了输入变量的维数,消除了各输入变量的相关性;另一方面提高了网络的收敛性和稳定性,同时也简化了网络的结构。通过实例验证,基于主成分的神经网络比一般神经网络训练精度更高,学习时间更短,预测效果更优。     

PCA-BP神经网络在降水预测中的应用研究

该文提出了一种基于主成分分析(PCA)与误差反向传播(BP)神经网络的降水预测模型。首先,通过主成分分析法对降水的主要影响因素进行主成分提取,选取贡献率大于85%的5个主成分来代替原来的14个影响因素,以消除原始输入数据的相关性,解决神经网络建模时输入变量过多、网络规模过大导致效率下降的问题;然后,以主成分分析结果为输入建立降水BP神经网络预测模型。仿真结果表明PCA-BP神经网络模型性能优于传统BP神经网络,能够满足降水预测的要求。     

面向云计算的主成分分析多变量局域预测模型

针对云平台无法从单变量负荷序列中获取完整预测信息的问题,提出了一种基于主成分分析的多变量局域预测模型并应用于云计算底层资源的预测中。利用主成分分析法综合考虑多种底层资源间的影响关系,确定多变量相空间的嵌入维数,并与局域预测法相结合,由此建立多变量局域预测模型。仿真实验表明,基于主成分分析的多变量局域预测模型的预测精度高于单变量局域预测模型,是面向云计算底层资源预测的一种有效方法。     

浮选生产过程经济技术指标的软测量建模

依据浮选过程的工艺机理和操作经验,初选了浮选过程经济技术指标神经网络软测量模型的输入变量,运用主元分析法对输入变量进行主元分解,降低输入变量维数且消除了输入变量之间的线性相关性,再通过基于最近邻聚类学习算法的径向基函数神经网络进行建模。仿真结果表明,该模型具有较快的训练速率和较高的预测精度,可以满足浮选过程实时控制的在线软测量要求。     

乙二醇生产过程中环氧乙烷浓度的软测量研究

环氧乙烷浓度是乙二醇生产过程中的1个重要指标,其浓度大小直接影响到后续水合反应生成乙二醇的过程。环氧乙烷浓度与多种因素之间存在着复杂的非线性关系,在软测量建模的过程中消除这些因素的相关性可以有效地降低计算复杂度。本文综合应用主元分析法,粒子群优化算法以及径向基函数神经网络建立了环氧乙烷浓度的软测量模型。首先分析了影响环氧乙烷浓度的因子,并对这些因子进行了主成分分析,得到1组新的输入因子。然后按照累积方差贡献率选取合适的输入因子,作为RBF神经网络的新的输入,有效降低了输入变量的维数,减少了输入变量之间的相关性,简化了神经网络的结构,建立了环氧乙烷浓度的软测量模型。最后利用粒子群算法来优化神经网络参数,求解RBF网络的径向基中心和输出层连接权值的最优值,减少了计算时间,提高了计算精度,获得了较好的拟合和预测效果。与只采用RBF网络建立软测量模型相比,本文采用的方法建模的误差较小,计算时间较短,计算精度较高,网络的预测效果较好。     

基于PCA-BP模型的高校教师职称评审预测

针对高校教师职称评审问题,提出基于PCA-BP的评审预测模型.采用主成分分析法对评审指标数据进行降维处理,选取保留原始指标信息的89.01％的四个主成分作为BP网络的输入,这样不仅减少了网络的输入维数,减小了网络训练规模,而且消除了各指标间的相关性,改善了网络的训练效率,提高了预测精度.利用Matlab软件对某高校2012年副教授评审实际数据的进行实例分析和仿真,并用该组数据比较该方法与典型BP网络的预测效果,结果表明该方法明显优于BP网络,完全能够满足职称评审预测的要求.     

基于主元分析的径向基神经网络预测模型研究

结合主元分析(PCA)和径向基函数(RBF)神经网络,建立了地下水动态模拟与软测量预测模型。通过主元分析法提取主要成分,实现数据预处理;将选取的主要成分作为RBF神经网络的输入;采用k均值聚类算法确定RBF网络隐含层参数,并用递进最小二乘法确定输出层权值。仿真结果表明,该模型优化了网络结构,提高了预测精度。     

PCA_RBF网络在电力负荷预测中的应用研究

研究电力负荷预测问题,由于电力负荷因子间存在非线性和高度冗余,传统方法无法消除数据之间冗余和捕捉非线性特征,导致预测精度较低.为了提高电力负荷预测精度,提出一种将主成份分析(PCA)和RBF神经网络相结合的电力负荷预测方法(PCA-RBF).首先对电力负荷高维变量数据矩阵进行标准化处理,然后利用主成分分析建立相关矩阵,计算特征值和特征向量,通过求取累计方差贡献率,对主成分作为RBF神经网络的输入进行训练预测,主成分以较少的维数包含了原高维变量所携带的大部分信息,避免过多的输入导致的精度低和训练慢的不足.采用PCA_RBF模型对某省1992-2002的电力负荷数据进行验证性测试和分析.实验结果表明,改进的PCA_RBF模型可有效降提高负荷预测精度.     

基于主成分分析和BP 神经网络的气体识别方法研究

本文将主成分分析法与BP算法相结合应用于气体传感器阵列信号的处理,并以一个由4个SnO2气体传感器组成的阵列为例,对其受到不同浓度的汽车、酒精二元气体的响应信号进行了分析,结果表明,主成分分析能够在保留测试数据量大量信息的前提下,给数据有效降维和预分类,以消除样本间的相关性。然后,再将所产生的新的样本空间作为BP网络输入,使之减少网络的输入数,简化网络结构,并在保持相同正确率的前提下,大大提高网络的学习速率。     

基于储备池主成分分析的多元时间序列预测研究

提出一种基于回声状态网络储备池的非线性ＰＣＡ 方法,并将其应用于多元时间序列的预测中．由于多维输入变量间的相关性会影响建模效果,通过储备池将输入在原空间的非线性特征转化成高维空间的线性特征．在其中运用线性ＰＣＡ 技术寻找输入在储备池空间的最大方差方向,提取有效的多元变量综合信息．经储备池主成分分析处理后的输入与预测点呈动态线性映射,可使用线性方法建模．仿真结果表明了该方法的有效性．     

优化的GEP算法在爆破振动预测中的应用

结合主成分分析和基因表达式编程,提出了一种基于PCA的优化基因表达式编程的新算法,并将其应用在爆破振动峰值速度和主频率的预测。该算法首先利用主成份分析方法对影响爆破振动的参数进行预处理,有效地减少预测模型的输入量,消除输入数据间的相关性,而后通过基因表达式程序设计建立爆破振动预测模型。结果表明,基于PCA的优化基因表达式编程算法比BP神经网络等其他算法得到的结果具有更高的预测精度和稳定性。     

Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis

In this paper, a novel methodology based on principal component analysis (PCA) is proposed to select the most suitable secondary process variables to be used as soft sensor inputs. In the proposed approach, a matrix is defined that measures the instantaneous sensitivity of each secondary variable to the primary variables to be estimated. The most sensitive secondary variables are then extracted from this matrix by exploiting the properties of PCA, and they are used as input variables for the development of a regression model suitable for on-line implementation.This method has been evaluated by developing a soft sensor that uses temperature measurements and a process regression model to estimate on-line the product compositions for a simulated batch distillation process. The identification of the optimal soft sensor inputs for this case study has been discussed with respect to the definition of the sensitivity matrix, the data sampling interval, the presence of measurement noise, and the size of the input set. The simulation results demonstrate that the proposed approach can effectively identify the size and configuration of the input set that leads to the optimal estimation performance of the soft sensor.     

Principal component analysis of fuzzy data using autoassociative neural networks

This paper describes an extension of principal component analysis (PCA) allowing the extraction of a limited number of relevant features from high-dimensional fuzzy data. Our approach exploits the ability of linear autoassociative neural networks to perform information compression in just the same way as PCA, without explicit matrix diagonalization. Fuzzy input values are propagated through the network using fuzzy arithmetics, and the weights are adjusted to minimize a suitable error criterion, the inputs being taken as target outputs. The concept of correlation coefficient is extended to fuzzy numbers, allowing the interpretation of the new features in terms of the original variables. Experiments with artificial and real sensory evaluation data demonstrate the ability of our method to provide concise representations of complex fuzzy data.     

Order estimation of multivariable ill-conditioned processes based on PCA method

Ill-conditioned multivariable processes exhibit significantly strong interactions among system variables and large gain directions from the system inputs to the outputs, which makes the identification and control a challenging task. The objective of this paper is to develop an order estimation algorithm for model identification of ill-conditioned processes using subspace methods. In this paper, the order is determined from noise-corrupted samples with high accuracy based on the principal component analysis (PCA) method. To excite each direction in the ill-conditioned process, test signals are designed carefully based on the system characteristics. Using the PCA modeling, the model prediction error is first reconstructed, and the Akaike Information Criterion (AIC) is then used to examine the modeling error bound and thus to determine the process order. A new weighted direction variable is proposed to strengthen the interactions along the small gain directions, thus improving the identifiability and accuracy of the ill-conditioned model. The effectiveness of the proposed method is confirmed by an application study on a high purity distillation column process under noise conditions.     

基于PCA的3种改进BP算法性能研究

现有入侵检测系统的效率和准确率较低。为此,提出一种基于主成分分析的特征提取方法。对数据源进行特征降维,将获得的主成分作为BP神经网络的输入数据进行识别。分析原始BP算法存在的问题,研究RPBP、CGBP、LMBP 3种改进BP算法,并进行仿真实验,结果表明,与原始BP算法相比,改进算法收敛速度快,漏报率和误报率低,能有效改善入侵检测的识别效果。     

A mobile robot mapping model inspired from the place cells functionality of hippocampus based on dimension reduction technique

ABSTRACT

In this paper, a new mobile robot mapping algorithm inspired from the functionality of hippocampus cells is presented. Place cells in hippocampus can store a map of the environment. This model fuses odometry and vision data based on dimensionality reduction technique, hierarchically. These two types of data are first fused and then considered as inputs to the place cell model. Place cells do the clustering of places. The proposed Place cell model has two types of inputs: Grid cells input and input from the lateral entorhinal cortex (LEC). The LEC is modelled based on the dimension reduction technique. Therefore, the data that causes locations different to be inserted into the place cell from this layer. Another contribution is proposing a new unsupervised dimension reduction method based on k-means. The method can find perpendicular independent dimensions. Also, the distance of cluster centres found in these dimensions is maximised. The method was compared with LDA and PCA in standard functions. Although LDA is a supervised method, the result showed that the proposed unsupervised method outperformed. To evaluate the place cells model, sequences of images collected by a mobile robot was used and similar results to real place cells achieved.     

Wavelets and non-linear principal components analysis for process monitoring

A non-linear principal component analysis (PCA) algorithm is proposed for process performance monitoring based upon an input-training neural network. Prior to assessing the capabilities of the monitoring scheme on an industrial dryer, the data is first pre-processed to remove noise and spikes through wavelet de-noising. The wavelet coefficients obtained are used as the inputs for the non-linear PCA algorithm. Performance monitoring charts with non-parametric control limits are then applied to identify the occurrence of non-conforming operation prior to interrogating differential contribution plots to help identify the potential source of the fault. Encouraging results were achieved.     

一种基于分级输入训练神经网络的非线性主元分析

基于输入训练神经网络的非线性主元分析（PCA）能够有效地提取过程变量的非线性主元,但是存在主元的个数不能通过网络训练确定,且各个主元重要程度在神经网络中无法区分等缺点，本文提出一种分级输入自调整神经网络,并进一步提出基于此网络的非线性PCA,通过多级输入自调整神经网络,将主元按顺序找出,且根据主元对过程数据的预测误差定量地确定出主元的个数,克服了上述缺点.