基于径基函数-加权偏最小二乘回归的干点软测量

针对影响石油馏分产品干点因素众多且呈高度非线性的特征，提出了一种径基函数(Radial basis function，RBF)和加权偏最小二乘回归(Weighted partial least squares regression，WPLSR) 相结合的建模方法建立干点软测量模型. 该组合方法首先应用RBF 实现样本数据的非线性变换；然后根据非线性变换后样本在结构参数空间中的分布，分析它们对预测对象的预报能力，自适应地为各个样本分配权值，并进而从中提取和选用PLS 成分，实施加权PLSR，以获得预报性能良好的模型. 在实际应用于初顶石脑油干点软测量建模中，RBF-WPLSR 获得比PLSR、WPLSR 及RBF-PLSR 更高精度的模型.     

基因表达式编程在环氧酶抑制剂定量构效关系中的应用

基因表达编程方法(GEP)是1种较新的进化计算算法,是1种较好的数据挖掘和建模工具。因其出色的数据挖掘能力并能以数学模型表达数据关系而广受关注,但在化学领域的应用还较少。本文应用GEP方法研究环氧酶抑制剂活性和选择性的构效关系(QSAR),选择变量和建立模型,同时与BP人工神经网络(BP-ANN)和遗传算法-多元线性回归(GA-MLR)方法比较。结果发现,GEP方法的预测较好,且模型稳定。研究显示GEP在定量构效关系研究中,具有广阔的应用前景。     

基于HGA优化RBF网络的污水总氮软测量

污水处理系统是一个包含海量信息的非线性复杂系统.目的是对某污水处理厂生物脱氮系统的出水总氮(TN)进行软测量建模.先用主元分析方法实现输入变量的降维和去相关,简化径向基函数(RBF)网络的输入.再应用递阶遗传算法(HGA)确定合理的RBF网络隐层节点数、基函数宽度和中心.能够同时优化网络参数和拓扑结构,在全局范围内寻找RBF参数的最优解,实现了RBF网络的自适应优化.应用该模型对出水总氮软测量进行仿真,结果表明了该网络模型的可靠和有效,说明该软测量模型具有工业应用价值和意义.     

基于RBF 神经网络的单元机组负荷系统建模研究

采用径向基函数(RBF)神经网络进行多变量系统的建模研究。将正规化正交最小二乘(ROLS)算法扩展到多输入多输出系统，建立多变量系统的RBF神经网络模型。对电厂单元机组负荷系统进行建模仿真研究的结果表明，用该方法建立的多变量热工系统的非线性模型是有效的，具有较高的辨识精度和较好的泛化能力。     

基于模糊模型和神经网络的多时滞不确定非线性系统的鲁棒H∞控制

针对一类具有多时滞的不确定非线性系统,提出了一种基于模糊模型和神经网络的组 合控制方法.利用具有多时滞的模糊T-S模型对系统进行近似建模并给出基于线性矩阵不等式 (LMI)的模糊H∞控制律.提出完全自适应RBF神经网络控制方法,通过在线自适应调整RBF 神经网络的权重、函数中心和宽度,来对消系统的未知不确定性和模糊建模误差的影响,不要求 系统的不确定项和模糊建模误差满足任何匹配条件或约束,并证明了闭环系统的稳定性.最后, 将所提出的方法应用到一具有多时滞的非线性混沌系统,仿真结果表明了该方法的有效性.     

PCA—RBF方法在聚丙烯熔融指数建模中的应用

针对聚丙烯的生产过程是一个大滞后、时变、非线性的复杂系统,提出了基于主成分分析(PCA)的RBF神经网络聚丙烯熔融指数建模方法。该方法用主元分析对高维输入变量进行预处理,构造反应过程信息的低维主元变量,再经径向基函数神经网络对主元变量进行建模。该方法不仅简化了神经网络的结构,而且可以借助主元分析方法对过程故障和过失误差进行侦破,避免导致模型的错误输出。理论分析和实验结果表明,基于PCA和RBF网络方法的聚丙烯熔融指数建模具有精度高、鲁棒性强的优点,有利于工业生产应用。     

分子结构网络静态参数用于食品防腐剂构效关系的研究

本文应用网络科学的理论与方法,将组成分子的原子看作是网络节点,而一个分子的平面结构图就是由这些节点通过边连接而成,从而构造出分子结构网络,进一步计算出分子结构网络的平均度、平均路程长度等18个网络静态特征变量,作为构效分析的候选自变量。为了更好的进行构效分析,利用BP(Back Propagate,反向传播)人工神经网络从18个候选自变量中筛选出对活性贡献大的接近度中心性最大值等4个自变量,分别用支持向量机回归(SVR)和BP神经网络对分子结构网络的4个自变量与大鼠经口毒性LD_(50)进行定量构效关系(QSAR)研究,实验结果表明:基于分子结构网络静态参数的支持向量机回归模型具有良好的预测能力。说明应用分子结构网络静态参数建模,对具有同一(或多种)属性/活性的一类物质进行构效关系分析研究的这种新方法具有一定的应用前景。     

人工神经网络方法研究含硫芳香衍生物毒性与结构的关系

结合逐步线性回归方法与人工神经网络方法，研究了含硫芳香衍生物对发光细菌毒性的构效关系，充分表明了两个方法的互补，回归方法为网络方法提供变量的物理解释，网络方法建立精确的构效关系模型，人工神经网络方法在非线民生较强的构效关系研究中起到重要的作用，基于交叉检验，本文还提出了防止人工神经网络方法过拟合现象发生的ｒ判据，对于建立较好的预报模型，具有一定的普遍意义。     

棉/涤混纺织物近红外简化分析模型构建方法研究

纺织品成分标签是纺织品技术法规严格控制的内容之一。论文采用近红外光谱分析技术对棉/涤混纺织物中棉含量进行分析。标准正态变量变换（SNV）用于消除由颜色、纹理和厚度等差异引起的纺织品光散射和光谱基线漂移;蒙特卡罗无信息变量消除（MCUVE）、竞争性自适应重加权算法（CARS）和迭代保留信息变量（IRIV）三种特征变量优选方法结合偏最小二乘回归（PLSR）用于棉含量近红外简化模型的构建;实验结果表明,经IRIV变量优选方法处理后,建模变量数减少到6,PLSR模型的RP和RMSEP分别为0.909和9.721。该方法为开发纺织品成分便携式近红外分析仪器的光源和探测器选用提供理论参考。     

基于投影寻踪的非线性鲁棒偏最小二乘法及应用

来自工业现场的数据往往具有非线性特性且包含离群点, 利用非线性偏最小二乘(partial least squares, PLS)建模易受离群点的影响. 针对这一问题, 结合径向基函数(radial basis function, RBF)网络, 本文提出了一种基于投影寻踪的非线性鲁棒PLS方法. 该方法首先利用RBF变换将自变量与因变间的非线性关系转化为线性关系; 然后利用投影寻踪算法提取变换后自变量的鲁棒偏最小二乘法成分; 最后建立鲁棒PLS成分与因变量之间的鲁棒线性回归模型. 将该方法应用于湿法冶金萃余液pH值软测量建模问题, 结果验证了其有效性.     

基于遗传算法的黄酮类化合物对PTKs抑制性的QSAR研究

用文献设定的结构参数和本文设定的结构参数,分别与由HyperChem7.5Student Evaluation计算得到的量化参数作为自变量构成2组数据,以逐步回归,遗传算法-偏最小二乘法(GA-PLS)和遗传算法-支持向量机(GA-SVM)等算法就黄酮类化合物对PTKs抑制性进行QSAR研究。用各算法模型处理数据,由本文设定的结构参数构成的数据集获得的预测结果更好,表明采用取代基团类型和取代位置结合的编码参数包含的信息更为丰富,对物质性质的描述更加合理。在各种算法中, GA-SVM模型均具有最佳预测效果,该算法对2组数据作留一法预测处理得到的相关系数R和PTKs抑制性实验值与预测值的平均绝对误差MAE分别为0.7595,0.2871和0.7864,0.2883。研究还表明,GA-PLS和GA-SVM联用算法的预测效果远高于单独使用的PLS和SVM算法;由逐步回归建立的MLR模型对2组数据进行计算处理,尽管拟合时相关系数R分别达到0.8136和0.8250,但作留一法交互验证时却下降到0.7113和0.7354,明显低于GA-PLS和GA-SVM联用算法。     

On the persistency of excitation in radial basis function networkidentification of nonlinear systems

Considers radial basis function (RBF) network approximation of a multivariate nonlinear mapping as a linear parametric regression problem. Linear recursive identification algorithms applied to this problem are known to converge, provided the regressor vector sequence has the persistency of excitation (PE) property. The main contribution of this paper is formulation and proof of PE conditions on the input variables. In the RBF network identification, the regressor vector is a nonlinear function of these input variables. According to the formulated condition, the inputs provide PE, if they belong to domains around the network node centers. For a two-input network with Gaussian RBF that have typical width and are centered on a regular mesh, these domains cover about 25% of the input domain volume. The authors further generalize the proposed solution of the standard RBF network identification problem and study affine RBF network identification that is important for affine nonlinear system control. For the affine RBF network, the author formulates and proves a PE condition on both the system state parameters and control inputs.     

Hierarchical genetic algorithm based RBF neural networks and application for modelling of the automatic depth control electrohydraulic system

The paper presents an approach to model nonlinear dynamic behaviors of the Automatic Depth Control Electrohydraulic System (ADCES) of a certain minesweeping weapon with Radial Basis Function (RBF) neural networks trained by hierarchical genetic algorithm. In the proposed hierarchical genetic algorithm, the control genes are used to determine the number of hidden units, and the parameter genes are used to identify center parameters of hidden units. In order to speed up convergence of the proposed algorithm, width and weight parameters of RBF neural network are calculated by linear algebra methods. The proposed approach is applied to the modelling of the ADCES, and experimental results clearly indicate that the obtained RBF neural network can emulate complex dynamic characteristics of the ADCES satisfactorily. The comparison results also show that the proposed approach performs better than the traditional clustering-based method.     

Elitist teaching–learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis

An adaptive p-step prediction model for nonlinear dynamic processes is developed in this paper and implemented with a radial basis function (RBF) network. The model can predict output for multi-step-ahead with no need for the unknown future process output. Therefore, the long-range prediction accuracy is significantly enhanced and consequently is especially useful as the internal model in a model predictive control framework. An improved network structure adaptation is also developed with the recursive orthogonal least squares algorithm. The developed model is online updated to adapt both its structure and parameters, so that a compact model structure and consequently a less computing cost are achieved with the developed adaptation algorithm applied. Two nonlinear dynamic systems are employed to evaluate the long-range prediction performance and minimum model structure and compared with an existing PSC model and a non-adaptive RBF model. The simulation results confirm the effectiveness of the developed model and superior over the existing models.

     

基于径向基函数神经网络的非线性模型辨识

从径向基函数（RBF）神经网络原理分析出发，提出了一种基于RBF神经网络学习算法，用于对非线性对象模型的拟合与辩识，并将此方法用于实际非线性模型的学习与辩识。结果表明，基于RBF的神经网络可快速完成对样本的学习与拟合，对具有连续特性的线性与非线性模型，具有快速实时的学习速度和优良的学习性能。     

RBF-ARX model-based MPC strategies with application to a water tank system

A hybrid pseudo-linear RBF-ARX model that combines Gaussian radial basis function (RBF) networks and linear ARX model structure is utilized for representing the dynamic behavior of a class of smooth nonlinear and non-stationary systems. This model is locally linear at each working point and globally nonlinear within whole working range. Based on the structural characteristics of the RBF-ARX model, three receding horizon predictive control (RBF-ARX-MPC) strategies are designed: (1) the RBF-ARX-MPC algorithm based on single-point linearization (MPC-SPL); (2) the RBF-ARX-MPC algorithm based on multi-point linearization (MPC-MPL); and (3) the RBF-ARX-MPC algorithm based on globally nonlinear optimization (MPC-GNO). In the MPC-SPL, the future multi-step-ahead predictive output of the system is obtained based on the local linearization of the RBF-ARX model at only current working-point, while in the MPC-MPL the future long-term output prediction is obtained according to the future local characteristics from previous online optimization results of the RBF-ARX model based MPC. In the MPC-GNO, the globally nonlinear characteristics of the RBF-ARX model are fully used for online getting control variables of the MPC. Real-time control experiments for the three type MPCs are carried out on a water tank system, which are also compared with a classical PID control and a traditional linear ARX model-based MPC. The results verify that the modeling method and the model-based predictive control strategies are realizable and effective for the nonlinear and unstable system. Moreover, it is also shown that the MPC-GNO can obtain better control performance but need more computation time compared to the other MPCs, which makes it possible to be applied into some slowly varying processes.