基于变量选择的转炉炼钢终点预报模型

转炉炼钢的终点预报模型对于钢水终点碳含量和温度的命中非常重要.针对高维输入不利于建立精确模型的问题,使用互信息方法对预报模型输入变量进行选择.为了区分各输入变量对输出的不同重要程度,对各输入变量进行加权处理,并采用微粒群算法对权值进行优化.最后,使用支持向量机方法建立转炉炼钢终点碳含量和温度预报模型.对一座180t转炉实际生产数据进行仿真,结果表明,合理的变量选择和加权处理能有效提高模型的预报精度.     

基于支持向量机碱度偏差估计的石灰加入量模型

提出碱度偏差估计模型,进而提出一种转炉炼钢石灰加入量计算方法.首先,使用支持向量机建立碱度偏差估计模型,预报输入碱度与炉渣碱度之间的偏差;然后,利用碱度偏差估计模型预报的偏差值修正传统公式中的碱度参数,再消除白云石加入量对石灰加入量的影响,得到改进的石灰加入量模型.应用该方法对一座150t转炉的实际生产数据进行计算,结果显示对碱度偏差的预报有着较高的精度,由此计算的石灰加入量可以满足实际生产的要求.     

支持向量机在网络异常入侵检测中的应用研究

研究网络安全问题,针对对网络异常入侵检测数据的特征进行提取,用传统异常入侵检测算法存在小样本情况下训练精度高,预测精度低的过拟合缺陷,出现误报和漏报现象,提出一种基于支持向量机的网络异常入侵检测方法.在支持向量机的网络异常入侵检测过程中,利用网格法寻找支持向量机最优参数,并找到的最优参数对网络异常入侵训练样本进行训练学习,得到最优异常入侵检测模型,对入侵检测数据进行预测.以网络异常入侵标准数据库DARPA中的数据集进行了仿真.仿真结果表明,小样本数据的支持向量机有较高的网络入侵检测准确率,具有较好的实时性,是一种高效、误报和漏报率低的网络异常入侵检测方法.     

电弧炉终点温度的GASVR_GM预报模型研究

电弧炉终点温度是炼钢过程中的重要指标之一,决定了钢水的质量和整体成本.电弧炉终点温度预报模型的建立是实现炼钢自动化的重要环节,为了得到高精度的终点温度预报值,提出了一种GASVR_GM的钢水终点温度预报模型.该模型以定量因素为主,采用遗传算法优化的支持向量回归机预报终点温度;再运用灰色模型进行预报误差的补偿,解决非定量因素的影响,实现滚动预报.试验仿真表明,与智能软测量方法相比,GASVR_GM预报模型具有更高的精度和鲁棒性.     

基于混合递阶遗传神经网络的转炉温度预报

针对转炉控制中对吹炼终点温度的控制问题,提出了基于混合递阶遗传RBF神经网络(HGA-RBF)的转炉炼钢终点温度预报模型.研究了RBF网络的特点,用递阶遗传算法克服了网络的结构和参数选择的随机性问题;并结合最小二乘法,提高了收敛速度.仿真结果表明,此算法在一定程度上提高了RBF网络的优化收敛速度和训练测试精度.某钢铁公司提供的实际冶炼数据试验,也证明了该模型预报精度较高,对提高生产的质量有重要意义.     

一种转炉炼钢动态终点预报的新方法

1　引言转炉炼钢就是将含有较多杂质的铁水与吹入的氧气发生反应 ,达到去除杂质的目的 ,从而获得要求的钢水成分和温度 .目前自动化炼钢的方法是静态控制和以副枪检测信息为基础的动态控制相结合的方法 .动态控制的关键是准确预报转炉炼钢终点温度和碳含量 .文 [1 ]在文 [2 ,3]的基础上采用 RBF神经网络对转炉炼钢终点温度和碳含量进行预报 ,提高了预报精度 .但是 ,终点温度和碳含量受到非定量因素的影响 ,文 [4 ]提出以灰色模型为基础的预报方法 ,由于应用线性回归补偿 ,因而影响预报精度 .本文将灰色模型与RBF神经网络相结合提出新的转…     

转炉炼钢智能动态终点控制

针对转炉炼钢过程复杂,影响终点因素多,而且难以进行连续准确针测量,提出了基于副枪检测信息的智能动态终点控制方法。采用灰色模型并通过神经网络进行补偿对转炉炼钢终点温度和碳含量进行预报,在此基础上,以RBF神经网络作为预设定模型,通过模糊调整确定补吹阶段需要的氧气量和加入的冷却剂量,并对一座180吨转炉进行仿真计算,结果表明了该方法的有效性。     

基于神经网络的转炉炼钢终点控制

转炉炼钢是一种非常重要的炼钢方法,终点控制是转炉炼钢后期的重要操作.由于冶炼过程温度极高,很难进行准确及时地测量,无法形成通常意义下的反馈控制.采用RBF神经网络预报转炉炼钢终点温度和碳含量,在此基础上提出了基于神经网络的动态终点控制方法来确定在补吹阶段需要的吹氧量和加入的冷却剂量,克服了传统控制方法中基于热平衡和氧平衡控制模型不准确的缺点,提高了终点命中率.     

转炉音频监控化渣及冶炼参数计算机管理系统

本文介绍了通过检测转炉炼钢过程中特征频带的信号,组成以计算机为中心的转炉造渣监控系统。该系统用图像的方法,比较完整地记录下吹炼过程的工艺参数及造渣过程,并对炼钢过程中发生的喷溅和反干现象及时作出预报,利用最佳化渣区指导炉前造渣操作。     

基于遗传算法优化SVM的嵌入式网络系统异常入侵检测

由于传统嵌入式网络系统入侵检测方法难以获得较高的检测精度,提出基于遗传算法优化的支持向量机(GA-SVM)的网络入侵检测技术.支持向量机分类器能够较好地解决少样本、高维、非线性分类问题.然而,支持向量机训练参数的选择对其分类精度有着很大影响,遗传算法能够同时优化支持向量机的训练参数,采用遗传算法进行支持向量机的训练参数同步优化.实验结果表明,这种遗传算法优化的支持向量机分类入侵检测模型有着很高的检测精度.     

Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine

This study concerns with the control of basic oxygen furnace (BOF) steelmaking process and proposes a dynamic control model based on adaptive-network-based fuzzy inference system (ANFIS) and robust relevance vector machine (RRVM). The model aims to control the second blow period of BOF steelmaking and consists of two parts, the first of which is to calculate the values of control variables, viz., the amounts of oxygen and coolant requirement, and the other is to predict the endpoint carbon content and temperature of molten steel. In the first part, an ANFIS classifier is primarily constructed to determine whether coolant should be added or not, then an ANFIS regression model is utilized to calculate the amounts of oxygen and coolant. In the second part, a novel robust relevance vector machine is presented to predict the endpoint. RRVM solves the problem of sensitivity to outlier characteristic of classical relevance vector machine, thus obtaining higher prediction accuracy. The key idea of the proposed RRVM is to introduce individual noise variance coefficient to each training sample. In the process of training, the noise variance coefficients of outliers gradually decrease so as to reduce the impact of outliers and improve the robustness of the model. Simulations on industrial data show that the proposed dynamic control model yields good results on the oxygen and coolant calculation as well as endpoint prediction. It is promising to be utilized in practical BOF steelmaking process.     

Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction

Basic oxygen furnace (BOF) steelmaking is a complex process and dynamic model is very important for endpoint control. It is usually difficult to build a precise BOF endpoint dynamic model because many input variables affect the endpoint carbon content and temperature. For this problem, two effective variables selection steps: mechanism analysis and mutual information calculation are proposed to choose appropriate input variables according to a variable selection algorithm. Then, the selected inputs are weighted on the basis of mutual information values. Finally, two input weighted support vector machine BOF endpoint dynamic models are constructed to predict endpoint carbon content and temperature. Results show that the variable selection for BOF endpoint prediction model is essential and effective. The complexity and precise of two endpoint prediction models are improved.     

一种新的回归型约简多分辨率相关向量机

提出一种新的稀疏贝叶斯回归算法.基于相关向量机,首先通过尺度核和小波核构造完备基以提高预测精度;然后利用保局投影对输入矩阵的列进行主成分提取以减少训练时间,从而形成算法的初步模型.为进一步减小较大规模训练数据集的回归时间压力,算法对训练数据集的分层采样建立了初步模型,进而产生实际较小规模的训练数据集.实验结果表明,算法在预测精度和鲁棒性上优于传统支持向量机和相关向量机,且其训练时间较相关向量机少.     

Hybrid robust support vector machines for regression with outliers

In this study, a hybrid robust support vector machine for regression is proposed to deal with training data sets with outliers. The proposed approach consists of two stages of strategies. The first stage is for data preprocessing and a support vector machine for regression is used to filter out outliers in the training data set. Since the outliers in the training data set are removed, the concept of robust statistic is not needed for reducing the outliers’ effects in the later stage. Then, the training data set except for outliers, called as the reduced training data set, is directly used in training the non-robust least squares support vector machines for regression (LS-SVMR) or the non-robust support vector regression networks (SVRNs) in the second stage. Consequently, the learning mechanism of the proposed approach is much easier than that of the robust support vector regression networks (RSVRNs) approach and of the weighted LS-SVMR approach. Based on the simulation results, the performance of the proposed approach with non-robust LS-SVMR is superior to the weighted LS-SVMR approach when the outliers exist. Moreover, the performance of the proposed approach with non-robust SVRNs is also superior to the RSVRNs approach.     

A novel support vector regression for data set with outliers

Support vector machine (SVM) is sensitive to the outliers, which reduces its generalization ability. This paper presents a novel support vector regression (SVR) together with fuzzification theory, inconsistency matrix and neighbors match operator to address this critical issue. Fuzzification method is exploited to assign similarities on the input space and on the output response to each pair of training samples respectively. The inconsistency matrix is used to calculate the weights of input variables, followed by searching outliers through a novel neighborhood matching algorithm and then eliminating them. Finally, the processed data is sent to the original SVR, and the prediction results are acquired. A simulation example and three real-world applications demonstrate the proposed method for data set with outliers.     

支持向量机组合分类及其在文本分类中的应用

针对标准支持向量机对野值点和噪音敏感，分类时明显倾向于大类别的问题，提出了一种同时考虑样本差异和类别差异的双重加权支持向量机。并给出了由近似支持向量机结合支持向量识别算法，识别野值点和计算样本重要性权值的方法．双重加权支持向量机和近似支持向量机组合的新分类算法尤其适用于样本规模大、样本质量不一、类别不平衡的文本分类问题．实验表明新算法改善了分类器的泛化性能。比传统方法具有更高的查准率和查全率．     

Fuzzy SVM with a New Fuzzy Membership Function to Solve the Two-Class Problems

In dealing with the Two-Class classification problems, the traditional support vector machine (SVM) often cannot achieve good classification accuracy when outliers exist in the training data set. The fuzzy support vector machine (FSVM) can resolve this problem with an appropriate fuzzy membership for each data point. The effect of the outliers can be effectively reduced when the classification problem is solved. In this paper, a new fuzzy membership function is employed in the linear and nonlinear fuzzy support vector machine respectively. The fuzzy membership is calculated based on the structural information of two classes in the input space and in the feature space. This method can distinguish the support vectors and the outliers effectively. Experimental results show that this approach contributes greatly to the reduction of the effect of the outliers and significantly improves the classification accuracy and generalization.     

转炉炼钢智能控制方法及应用

将模糊推理、自适应、自学习、专家系统等技术与转炉炼钢动态控制方法相结合,提出了转炉炼钢智能动态控制的新方法.该控制方法由动态吹炼过程补吹氧量与冷却剂量预设定模型、熔池碳含量和温度预测模型及停吹专家系统等组成,并成功应用于某钢厂转炉炼钢过程,大大提高了转炉炼钢过程熔池命中率.     

基于预测值替代的相关向量回归稳健化方法

针对相关向量机的性能易受到奇异值影响的情况,提出了一种增强相关向量机稳健性的方法。其主要思想如下:首先用原始训练数据训练相关向量机;然后,利用某种准则,从原始数据中挑选一些样本,用其预测值代替输出变量值;随后,用改变后的训练样本重新训练相关向量机。这个过程可重复几次。数据试验表明,较之相关向量机和变分稳健相关向量机,新算法对奇异值更加不敏感。