间歇硫酸盐法蒸煮过程纸浆卡伯值的近红外光谱法在线测量的研究

研究发现，硫酸盐法蒸煮液在800-900nm近红光谱波段间的吸收主要来自其中的碱木素，而其中的D-葡萄糖、D-甘露糖、D-木糖、D-半乳糖和糖醛酸等碳水化合物的降解产物及二氯甲烷和苯醇抽出物在此波段基本上没有吸收。并在这段波段建立起的纸浆卡伯值近红外光谱测量模型。该模型显示蒸煮液的吸光度与纸浆卡伯值具有较好的线性关系（相关系数R^2为0．996），并有较高的预测精度。因此，建议采用近红外光谱法实现硫酸盐法蒸煮过程纸浆卡伯值的在线测量，并有望开发出蒸煮过程纸浆卡伯值近红外光谱法在线测量仪，真正实现蒸煮过程的纸浆卡伯值的闭环反馈控制。     

光谱法测量间歇蒸煮过程纸浆卡伯值建模方法的研究

纸浆的卡伯值是蒸煮过程的重要质量指标，为合理控制蒸煮工艺条件需要在线测量蒸煮过程中纸浆的卡伯值。在线光谱法测量卡伯值需以工业现场数据为基础，对可见光吸收光谱法预测亚硫酸盐法间歇蒸煮过程中纸浆卡伯值进行建模研究，经过多种统计模型的比较，发现偏最小二乘法回归模型能够提高预测精度，增强模型的适应性和稳定性，这一方面验证了光谱法的可行性，另一方面也显示光谱法有待完善。希望通过对光谱测量机理的研究，提高光谱信息的利用率。     

GA-BP网络在凿岩防卡阀推进压力预测中的应用

分析了凿岩钻车防卡阀的结构和工作原理,利用某采石场原始卡钎数据,建立了防卡阀BP神经网络模型。基于遗传算法理论对BP神经网络模型进行了结构拓扑优化和训练,建立了GA-BP网络模型。分析结果表明,BP神经网络模型和GA-BP网络模型均可以较好地预测卡钎时防卡阀的推进压力,但GABP网络模型具有更高的预测精度、非线性映射和网络性能。     

GRNN神经网络在信息分析预测中的应用

用广义回归网络模型建立糖尿病和高脂血症预测网络模型,结果准确率高,达到了预测的目的.避免了BP网络预测同样的数据库,算法冗长,网络预测结果不稳定的缺点.经过对比,GRNN网络具有更好的拟合精度和预报精度.实例分析证明,广义回归网络模型可以应用于疾病预测数据处理工作,并可以取得更优的分析结果.     

基于PCA-SVM的柴油凝点近红外光谱软测量法

提出一种基于PCA-SVM及近红外光谱(NIRS)分析技术的柴油凝点软测量方法。首先,采用多项式卷积对原始的柴油NIRS数据进行光谱平滑、基线校正和标准归一化;然后,利用主元分析(PCA)对近红外光谱数据集的高维特征进行组合并向低维空间投影;最后,利用SVM回归算法建立凝点的软测量模型。与BP、SVM及PCA-BP方法相比,实验结果表明所提方法具有更高的测量精度,且与标准方法测量的结果更为接近,因此适合柴油凝点的在线测量。     

SMB色谱分离过程组分纯度软测量技术

针对SMB色谱分离过程中组分纯度的实时测量存在困难的现状,建立了两组分(葡萄糖、果糖)纯度的在线软测量模型。软测量模型采用NNARMAX模型作为模型辨识类;采用BP神经网络对模型进行逼近,为加快网络收敛速度,采用Levenberg—Marquardt算法对网络进行训练。在Matlab工作平台上进行了大量的仿真,对该模型进行验证,仿真结果证明了该方法的有效性。     

杜仲松脂醇二葡萄糖苷含量的近红外光谱检测研究

采用AntarisⅡ傅立叶变换红外光谱分析仪器(Thermo Nicolet)测定了9种杜仲的光谱数据,运用偏最小二乘法(PLS)和主成分分析回归(PCR)分别建立了杜仲中松脂醇二葡萄糖苷(PDG)含量与吸光度变量的近红外光谱定标模型,并对所建模型进行验证.结果表明,2种方法建立的模型精度都较高,其中模型的预测能力强弱指标SSE=2.8333,PRESS=7.2392,可见在近红外光谱下,PCR和PDG都适合对杜仲进行检测研究,这是本文的一个重要结果,为以后杜仲的指标检验研究提供了理论和实验依据:但是,PCR建立的(PDG)定标模型预测精度稍高于偏最小二乘法(PLS)建立的模型,所以文中只对PCR进行模型的建立,PLS方法对杜仲检测分析这个问题有待于以后的进一步讨论和研究.     

基于粒子群优化神经网络(PSO-BPNN)算法在发酵过程控制中的研究

针对发酵过程中生物参数难以实时在线测量的问题,建立了用于生物参数状态预估的PSO-BPNN软测量模型。鉴于标准BP神经网络收敛太慢的缺点,运用PSO算法来优化网络权值,在此基础上,以饲料用β-甘露聚糖酶为对象,建立其基于PSO-BPNN的发酵过程产物浓度状态预估模型。发酵罐控制结果表明:该模型具有很好的学习精度和泛化能力,可实现对β-甘露聚糖酶产物浓度的实时在线预估。     

基于PCA-GABP神经网络的BOD软测量方法

针对污水处理过程中关键水质参数无法在线监测的问题．提出基于PCA-GABP神经网络的污水水质软测量方法。该方法由两部分组成：主元分析PCA和GABP神经网络。其中,GABP算法采用局部改进遗传算法优化神经网络权值。并采用自适应学习速率动量梯度下降算法对神经网络进行训练,建立软测量模型。仿真结果表明该软测量模型稳定性好、精度高,可用于污水处理厂对BOD进行在线预测。     

煤矿瓦斯涌出量动态预测的PCA-MFOA-GRNN模型及应用

针对煤矿瓦斯涌出受许多因素的影响,为了克服瓦斯涌出中存在的复杂的非线性关系,从而实现稳定、可靠、精确的对煤矿综采工作面瓦斯涌出量进行动态预测,提出了主成分分析法（PCA）结合改进的果蝇算法（MFOA）优化GRNN的绝对瓦斯涌出量的预测手段。运用PCA算法对原始输入数据降维;并且对果蝇算法中的Si函数增加一个跳脱参数B,避免局部最优因子对预测模型的干扰;将MFOA算法对GRNN的平滑因子σ进行优化;将PCA结果作为模型的输入,建立了PCA-MFOA-GRNN算法的回采工作面瓦斯涌出量动态预测模型,结合实际矿井瓦斯涌出量监测的相关数据检验该模型,并将该模型的预测结果与未修正的FOA-GRNN算法、CIPSO-ENN算法、BP神经网络预测、Elman网络预测结果进行对比,结果表明：该预测模型对GRNN的参数优化后得到的预测模型较其他预测模型有更强的泛化能力和更高的预测精度。     

Growing radial basis neural networks: merging supervised andunsupervised learning with network growth techniques

This paper proposes a framework for constructing and training radial basis function (RBF) neural networks. The proposed growing radial basis function (GRBF) network begins with a small number of prototypes, which determine the locations of radial basis functions. In the process of training, the GRBF network gross by splitting one of the prototypes at each growing cycle. Two splitting criteria are proposed to determine which prototype to split in each growing cycle. The proposed hybrid learning scheme provides a framework for incorporating existing algorithms in the training of GRBF networks. These include unsupervised algorithms for clustering and learning vector quantization, as well as learning algorithms for training single-layer linear neural networks. A supervised learning scheme based on the minimization of the localized class-conditional variance is also proposed and tested. GRBF neural networks are evaluated and tested on a variety of data sets with very satisfactory results.     

Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

一种多层前馈网参数可分离学习算法

目前大部分神经网络学习算法都是对网络所有的参数同时进行学习.当网络规模较 大时,这种做法常常很耗时.由于许多网络,例如感知器、径向基函数网络、概率广义回归网络 以及模糊神经网络,都是一种多层前馈型网络,它们的输入输出映射都可以表示为一组可变 基的线性组合.网络的参数也表现为二类:可变基中的参数是非线性的,组合系数是线性的. 为此,提出了一个将这二类参数进行分离学习的算法.仿真结果表明,这个学习算法加快了学 习过程,提高了网络的逼近性能.     

RBF网络光度法与BP网络光度法的比较

目的：探讨神经网络光度法用于复方制剂的含量测定。方法：训练集为按L25（5^6）正交表制备的25组标准混合液的吸光度数据和各组分的浓度数据，混合液中各组分的5个浓度水平分别为80％、90％、100％，110％和120％。预报集采用复方制剂的吸光度数据。网络的输入为混合物的吸光度，网络的输出为各组分的浓度。分别用径向基函数网络和Levenberg-Marqurdt优化算法的BP网络处理数据。结果：复方阿司匹林片和联磺甲氧苄啶片的紫外分光光度法测定结果表明，径向基函数网络在网络训练时间和测定精度等方面好于Levenberg-Marqurdt优化算法的BP网络。结论：径向基函数网络光度法测定复方制剂简便，准确。     

Uncertain data stream algorithm based on clustering RBF neural network

In this paper, the existing algorithms for modeling uncertain data streams based on radial basis function neural networks have problems of low accuracy, weak stability and slow speed. A new clustering method for uncertain data streams is proposed. Radial basis function neural network of the algorithm. The algorithm firstly models the uncertain data stream, then combines the fuzzy theory and the neural network principle to obtain the radial basis function neural network, and then obtains the radial basis function neural network through the clustering algorithm of the regular tetrahedral uncertain vector. The central weight and width weights ultimately result in hidden layer output and output layer output results. The experimental results show that the proposed algorithm is an effective algorithm for modeling uncertain data streams using clustering radial basis function neural networks. It has higher precision, stability and speed than similar algorithms.     

Prediction of reservoir sensitivity using RBF neural network with trainable radial basis function

Reservoir sensitivity prediction is an important basis for designing reservoir protection program scientifically and exploiting oil and gas resources efficiently. Researchers have long endeavored to establish a method to predict reservoir sensitivity, but all of the methods have some limitations. Radial basis function (RBF) neural network, which provided a powerful technique to model non-linear mapping and the learning algorithm for RBF neural networks, corresponds to the solution of a linear problem, therefore it is unnecessary to establish an accurate model or organize rules in large number, and it enjoys the advantages such as simple network structure, fast convergence rate, and strong approximation ability, etc. However, different radial basis function has different non-linear mapping ability, and different data require different radial basis functions. Nowadays, the choice of radial basis function in the network is based on experience or test result only, which exerts a great adverse impact on the network performance. In this study, a new RBF neural network with trainable radial basis function was proposed by the linear combination of common radial basis functions. The input parameters of the network were the influence factors of reservoir sensitivity such as porosity and permeability, etc. The output parameter was the corresponding sensitivity index. The network was trained and tested with the data collected from our own experiments. The results showed that the new RBF neural network is effective and improved, of which the accuracy is obviously higher than the network with single radial basis function for the prediction of reservoir sensitivity.     

基于PLS和GAs的径基函数网络构造策略

鉴于传统径基函数网络(radial basis function network,简称RBFN)构造策略的不足,提出了基于偏最小二乘法(partial least squares,简称PLS)和遗传算法(genetic algorithms,简称GAs)的RBFN构造策略和一种更有效的径基宽度取值方法.在这个集成构造策略中,PLS克服了K-Means算法求取径基易陷入局部最优的弊病,并使合成径基比由正交算法获取的径基更具代表性;而所提出的径基宽度取值方法和GAs则为网络性能和结构的实质性改善与优化提供了保障.实验证实了基于PLS和GAs的RBFN构造策略及所提出的径基宽度取值方法的优越性、可靠性和有效性.     

基于微种群遗传算法和RBF神经网络的散乱数据重建方法

利用微种群遗传算法,结合性能优越的径向基函数神经网络,建立了适用于散乱数据曲面重建的径向基函数网络模型.采用微种群遗传算法完成对神经网络的权值优化,可避免早熟收敛,且有较快的收敛速度.实验结果表明,用这种方法解决散乱数据点的重建问题,具有较高的精度.     

Non-linear systems identification using radial basis functions

This paper investigates the identification of discrete-time non-linear systems using radial basis functions. A forward regression algorithm based on an orthogonal decomposition of the regression matrix is employed to select a suitable set of radial basis function centers from a large number of possible candidates and this provides, for the first time, fully automatic selection procedure for identifying parsimonious radial basis function models of structure-unknown non-linear systems. The relationship between neural networks and radial basis functions is discussed and the application of the algorithms to real data is included to demonstrate the effectiveness of this approach.