基于剪接系统的遗传算法RBF网络建模方法

A splicing system based genetic algorithm is proposed to optimize dynamical radial basis function （RBF） neural network, which is used to extract valuable process information from input output data. The novel RBF network training technique includes the network structure into the set of function centers by compromising between the conflicting requirements of reducing prediction error and simultaneously decreasing model complexity. The effectiveness of the proposed method is illustrated through the development of dynamic models as a benchmark discrete example and a continuous stirred tank reactor by comparing with several different RBF network training methods.     

基于模糊RBF神经网络的乙烯装置生产能力预测

针对传统的径向基函数(RBF)神经网络隐藏层节点的不确定和初始中心敏感性、收敛速度过慢等问题,提出一种基于模糊C均值的RBF神经网络(FCM-RBF)模型,通过模糊C均值聚类(FCM)得到各聚类中心,基于误差反传的梯度下降法训练隐藏层到输出层之间的权值,克服传统RBF模型对数据中心的敏感性,优化确定RBF神经网络隐藏层的节点数,提高网络训练速度和精度。最后将其用于乙烯装置生产能力预测中,分析预测不同技术、不同规模乙烯装置生产情况,指导乙烯生产,提高生产效率,结果验证了所提出算法的有效性和实用性。     

基于EGK'M-RBF网络的顺丁橡胶门尼黏度预测

提出一种基于增强的全局K'-means算法(EGK'M)-RBF网络的建模方法,该方法采用作者提出的EGK'M来确定RBF网络隐含层的结构,包括隐含层中心个数、中心位置以及隐含层扩展常数,采用KPCA提取非线性特征信息,实现辅助变量的二次选择.并与基于PCA和EGK'M-RBF网络模型、基于KPCA和K-means算法...     

Melt index prediction by RBF neural network optimized with an adaptive new ant colony optimization algorithm

Melt index (MI) is a crucial indicator in determining the product specifications and grades of polypropylene (PP). The prediction of MI, which is important in quality control of the PP polymerization process, is studied in this work. Based on RBF (radial basis function) neural network, a soft‐sensor model (RBF model) of the PP process is developed to infer the MI of PP from a bunch of process variables. Considering that the PP process is too complicated for the RBF neural network with a general set of parameters, a new ant colony optimization (ACO) algorithm, N‐ACO, and its adaptive version, A‐N‐ACO, which aim at continuous optimizing problems are proposed to optimize the structure parameters of the RBF neural network, respectively, and the structure‐best models, N‐ACO‐RBF model and A‐N‐ACO‐RBF model for the MI prediction of propylene polymerization process, are presented then. Based on the data from a real PP production plant, a detailed comparison research among the models is carried out. The research results confirm the prediction accuracy of the models and also prove the effectiveness of proposed N‐ACO and A‐N‐ACO optimization approaches in solving continuous optimizing problem. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

一种改进的支持向量回归的混凝土强度预测方法

混凝土抗压强度是影响建筑质量的主要因素,根据一些主要参数事先预测其强度可作为现场施工的参考.以支持向量回归(SVR)为理论基础,提出一种基于马氏距离的加权型SVR(MWSVR)的人工智能算法对混凝土强度进行预测.不同于将训练样本统一看待的传统方法,该算法根据训练集和测试集自变量的距离来决定训练样本在求解SVR模型中的重...     

改进的双模型结构RBF神经网络及其应用

提出了离线结构学习和在线权值校正相结合的双模型结构RBF神经网络,以离线学习和在线校正相结合的方式实现网络的自学习和自校正,满足了软测量仪表现场应用的要求。针对应用过程中出现预测误差过大的现象,通过对网络算法进行分析,研究影响网络预测精度的因素,在此基础上,提出了以K均值聚类法和递推下降算法相结合的RBF神经网络建模改进算法,仿真结果和实际应用证明了改进算法的有效性。     

基于互信息和自组织RBF神经网络的出水BOD软测量方法

针对污水处理过程出水生化需氧量（biochemical oxygen demand，BOD）难以实时准确测量的问题，提出了一种基于互信息和自组织RBF神经网络的软测量方法对出水BOD进行预测。首先，使用基于互信息的方法提取相关特征参量作为软测量模型的输入变量；其次，设计一种基于误差校正-敏感度分析的自组织RBF神经网络，使用改进的Levenberg-Marquardt（LM）算法对网络进行训练以提高训练速度；最后将软测量模型应用于UCI公开数据集及实际的污水处理过程，实验结果表明该软测量模型结构紧凑，训练时间相对较短，预测精度有所提高，能够对出水BOD实现快速准确预测。     

基于径向基函数网络的MH/Ni电池建模及容量预测

引　言近年来 ,随着汽车的迅速发展和大量普及 ,它所造成的尾气污染问题也日益突出 .电动车的发展可以有效地解决燃油汽车的污染排放问题 .MH/Ni电池是一种无污染的“绿色能源” ,它具有高比能量、高比功率、长寿命及安全性好等特点 ,是电动车用动力型电池的首选 .在动力型电池     

基于改进遗传算法的BP神经网络手写数字识别

手写数字识别是光学字符识别技术的一个分支,一般采用神经网络,其中较为突出的是BP神经网络,但BP算法易陷入误差局部最小产生振荡且训练速度慢,通常先采用优化算法对其结构进行优化。为此,在分析GA-BP算法原理的基础上,提出对GA算法的相应算子中交叉和变异概率进行改进的方法,并用改进的GA算法优化BP神经网络的连接权值和阈值。以手写体数字识别为对象进行实验,结果表明：该方法具有更快的收敛速度和更可靠的稳定性,大大提高了BP神经网络的学习速度和识别率。     

优化增量型随机权神经网络及应用

针对传统增量型随机权神经网络(I-RVFLNs)存在网络参数难以优化确定、模型收敛速度慢和结构复杂的问题,提出一种优化增量型随机权神经网络算法,即O-I-RVFLNs。与传统I-RVFLNs不同,所提O-I-RVFLNs算法首先设定了一个期望的建模残差向量,然后在每次新增隐层节点时,选择可以达到或小于此节点期望残差的输入权值和偏置作为该节点的输入参数,进而提高网络的收敛速度。除此之外,考虑到算法在不断迭代更新过程中建模误差越来越小,下降趋势越来越不明显的问题,将各指标参数相邻两次迭代均方根误差的差值考虑在算法终止条件内,并借鉴统计过程控制中的西电规则制定了相应的算法收敛判定准则。最后,基于UCI能效数据和实际高炉工业数据,对所提O-I-RVFLNs算法进行了验证和应用。结果表明,相对于其他RVFLNs算法,所提算法建立的数据模型能够获得更紧凑的网络结构以及更好的泛化性能和预测精度。     

输入训练神经网络的维数约简算法及其在化工过程建模中的应用

Many applications of principal component analysis （PCA） can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on improved input training neural network （IT-NN） is proposed for the nonlinear system modelling in this paper. Momentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network （ANN）, IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network （RBF-NN） to model the yields of ethylene and propylene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.     

一种基于新型蚁群算法的聚丙烯熔融指数预报模型

聚丙烯熔融指数的实时预报非常重要却十分困难,提出了一种经过新型蚁群算法优化后的PCA-RBF神经网络方法进行熔融指数预报。PCA将原始数据从高维空间映射到低维空间,剔除冗余信息和提取过程特征;RBF神经网络则用来拟合输入与输出之间的非线性关系;最后用适用于连续空间寻优问题的新型蚁群算法对RBF神经网络权值进行优化。实际生产数据的研究结果,表明了所提出的熔融指数预报模型的准确性和可靠性。     

一种不等长的多模态间歇过程故障检测方法

提出一种不等长的多模态间歇过程故障检测方法。首先,运用局部加权算法对不等长批次数据进行预处理。在训练样本中确定不等长数据的最大可保留长度,利用k近邻信息,通过加权重构出不等长批次缺失的数据点。其次,对等长的训练集构造局部近邻标准化矩阵,运用K-means算法进行模态聚类,使用局部离群因子方法确定第一控制限,并剔除离群样本。最后,对各个模态建立MPCA模型并确定第二控制限。根据各个模态控制限的匹配系数计算统一的统计量和控制限,在统一的控制限下进行多模态故障检测。将提出方法应用于半导体工业过程,仿真结果表明,与传统的故障检测算法相比,本文算法提高了故障检测率,验证了该方法的有效性。     

Machine learning modelling and evaluation of jet fires from natural gas processing,storage, and transport

Jet fires and their repercussions play a significant role in catastrophic incidents that typically have a cascading impact in process industries. Several hydrocarbon experiments from 19 papers were incorporated into the current endeavour to develop simulations of jet flames using machine learning (ML) models. Dimensionless characteristics have been used as output and input variables, including mass flow rates, fuel density, jet flame length, and heat release fluxes. When training three layers of the multi-layer feedforward neural network (MLFFNN) method, a Bayesian regularization backpropagation approach was adopted and evaluated with the radial based functions (RBF) algorithm. Through an optimization procedure, the first and second hidden layers of the MLFFNN have been optimized to include 10 and five neurons, respectively. The RBF algorithm with 40 neurons in a single layer has been computed using the same method. The best mean square error (MSE) validation results for RBF and MLFFNN were 0.006 and 0.0002, respectively, for 40 and 100 epochs. The MLFFNN and RBF models' respective regression statistical analysis outputs were 0.9949 and 0.9645. The ML method has been identified as a potentially useful technique for precisely predicting the geometrical and radiative characteristics of jet flames.     

基于粗糙集和RBF神经网络的风电场短期风速预测模型

结合粗糙集提出了一种RBF神经网络短期风速预测模型。采用粗糙集对预测模型的输入特征空间进行约简,找出对未来预测的风速具有主要影响的因素,以此作为RBF神经网络预测模型的输入变量;在RBF神经网络训练的过程中,采用在线滚动优化策略,将最新的样本加入训练集,从而使预测模型能够跟踪风速的最新变化。将提出的方法用于某风电场的1 h短期风速预测,仿真实验结果表明该方法具有结构简单、预测精度高的优点。     

基于优化神经网络的高拱坝力学参数反演

高拱坝力学性能参数变化规律复杂,使用人工智能算法进行预测已经成为反演参数的重要手段。使用遗传算法对神经网络进行优化来检验优化后算法的性能,并比较不同算法应用于参数反演中预测结果的精度。根据某高拱坝运行期变形监测数据,分别使用RBF神经网络和遗传算法优化的BP(GA-BP)神经网络对不同水位工况下的坝段分区混凝土弹性模量进行反演。基于反演结果进行有限元正分析计算,将所得结果与实测数据进行对比,检验反演精度和效率。结果表明:GA-BP网络的最大预测误差为1.8%,相比于RBF网络预测精度提高了约50%。使用神经网络进行拱坝力学参数反演实用性好,优化后的神经网络比传统BP神经网络在计算精度和效率两方面均有明显改进,且GA-BP神经网络反演比RBF神经网络反演精度更高。     

DOBD Algorithm for Training Neural Network: Part I. Method

Overfitting is one of the important problems that restrain the application of neural network. The traditional OBD (Optimal Brain Damage) algorithm can avoid overfitting effectively. But it needs to train the network repeatedly with low calculational efficiency. In this paper, the Marquardt algorithm is incorporated into the OBD algorithm and a new method for pruning network－the Dynamic Optimal Brain Damage (DOBD) is introduced. This algorithm simplifies a network and obtains good generalization through dynamically deleting weight parameters with low sensitivity that is defined as the change of error function value with respect to the change of weights. Also a simplified method is presented through which sensitivities can be calculated during training with a little computation. A rule to determine the lower limit of sensitivity for deleting the unnecessary weights and other control methods during pruning and training are introduced. The training course is analyzed theoretically and the reason why DOBD algorithm can obtain a much faster training speed than the OBD algorithm and avoid overfitting effectively is given.     

The Development of Intelligent Models for Liquid–Liquid Equilibria (LLE) Phase Behavior of Thiophene/Alkane/Ionic Liquid Ternary System

ABSTRACT

This paper develops three models based on artificial neural network (ANN), support vector machine (SVM) and least square support vector machine (LSSVM) algorithm for phase behavior of thiophene/alkane/ionic liquid ternary system. The shuffled complex evolution (SCE) was employed to acquire the optimal magnitudes of hyper parameters (σ² and γ) which are embedded parts of SVM and LSSVM models, and the trial and error was employed to obtain the optimal numbers of neuron and layers for ANN intelligent model. Gathering and using 618 LLE data, the comparison between the optimized version of applied intelligent models in giving the LLE was also made. The findings are indicative of a prefect agreement between the estimation from intelligent models and the experimental data. The finding also reveals that the performance of SVM in prediction of solubility is somewhat better than other intelligent models (i.e., ANN and SVM) as coefficient determination (R2) and root mean squared error (RMSE) are respectively 0.9961 and 0.0447 for test sets of data. This is likely due to the existence of structural risk minimization principle of SVM which is embodied in SVM algorithm and effectively minimizes upper bound of the generalization error, rather than minimizing the training error.     

RBF-CSR方法及其应用于裂解装置建模的研究

RBF－CSR是在分析RBF－PLS的基础上提出的新方法。它保留了RBF－PLS的优点：采用神经网络的结构，又用数学方法直接求解，免去了ANN冗长的训练过程和其它诸多欠缺。RBF－CSR方法可以在更宽广的空间内寻找最优的网络参数，它所建立的模型具有很强的预报精度和良好的稳定性，又有简洁的解析形式，便于优化等进一步的计算和处理。该方法已成功地应用于裂解装置的建模。     

即时局部建模在填料塔液泛气速预测的应用

填料塔在工业生产中应用广泛,准确预测填料塔的液泛气速具有重要的应用价值。实际的填料类型多种多样,获取的填料数据也存在差异,单一全局模型的预测效果受到一定的限制。首先给出了岭参数极限学习机模型及其节点增加的递推算法,以有效更新在线模型。结合即时学习方式,提出了局部递推岭参数极限学习机在线建模方法,用于填料塔液泛气速的预测。实验结果表明所提出方法能更充分挖掘数据间的相关信息,预测效果优于相应的全局模型。