CN109002862A - A kind of flexible measurement method neural network based and system towards copper ore floatation machine - Google Patents
A kind of flexible measurement method neural network based and system towards copper ore floatation machine Download PDFInfo
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- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 2
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Abstract
本发明涉及一种面向铜矿浮选机的基于神经网络的软测量方法及系统,其特征在于,包括以下步骤:S1:RBF神经网络学习算法步骤,基于K‑均值聚类方法求取基函数中心c,NNG算法对RBF神经网络进行输入变量压缩;S2:最优NNG算法参数的选择及误差预测;S3:NNG‑RBF算法建模。
The present invention relates to a neural network-based soft sensor method and system for copper ore flotation machines, characterized in that it comprises the following steps: S1: RBF neural network learning algorithm step, based on the K-means clustering method to obtain the base function Center c, NNG algorithm compresses input variables of RBF neural network; S2: Selection of optimal NNG algorithm parameters and error prediction; S3: Modeling of NNG‑RBF algorithm.
Description
技术领域technical field
本发明属于软件测量技术领域,涉及一种基于神经网络的软测量方法及系统,尤其是一种面向铜矿浮选机的基于神经网络的软测量方法及系统。The invention belongs to the technical field of software measurement, and relates to a neural network-based soft measurement method and system, in particular to a neural network-based soft measurement method and system for copper ore flotation machines.
背景技术Background technique
在现代工业生产中,为获得更多合格的高质量产品,进而提高经济效益,就需要对产品质量或与产品质量密切相关的重要过程变量进行严格控制。In modern industrial production, in order to obtain more qualified high-quality products and improve economic benefits, it is necessary to strictly control product quality or important process variables closely related to product quality.
图1为铜矿浮选机装置矿化过程分区图,该装置有混合区、运输区、分离区和泡沫区四个区域,用来分离矿浆中的硫和铜,使矿物一步一步得到富集,保证泡沫层中的矿物不致脱落,泡沫能顺利地流入泡沫槽内。Figure 1 is the zoning diagram of the mineralization process of the copper ore flotation machine device. The device has four areas: mixing area, transportation area, separation area and foam area, which are used to separate sulfur and copper in the pulp, so that minerals can be enriched step by step , to ensure that the minerals in the foam layer will not fall off, and the foam can flow into the foam tank smoothly.
为了保证产品质量并防止矿浆中铜和硫的流失,需对该装置的PH值进行实时检测,以使PH值保持在一定范围内。然而,由于铜矿矿浆的高黏稠性,PH计容易被沉淀的矿浆包住、结块从而无法进行准确、实时的检测。In order to ensure the product quality and prevent the loss of copper and sulfur in the pulp, it is necessary to detect the pH value of the device in real time to keep the pH value within a certain range. However, due to the high viscosity of the copper ore slurry, the pH meter is easily wrapped and agglomerated by the precipitated slurry, so that accurate and real-time detection cannot be performed.
在线装置不能很好的达到要求,因此需要一种代替在线分析仪表的软测量方法。而在面对许多复杂的输入变量时,如何快速准确的实现对多个输入变量进行有效筛选和系数压缩,以及对该浮选机装置PH值的预测变得非常困难。The online device can't meet the requirements very well, so a soft measurement method is needed to replace the online analysis instrument. However, in the face of many complex input variables, how to quickly and accurately realize the effective screening and coefficient compression of multiple input variables, and the prediction of the pH value of the flotation machine device has become very difficult.
此为现有技术的不足之处。因此,针对现有技术中的上述缺陷,提供设计一种面向铜矿浮选机的基于神经网络的软测量方法及系统,以解决现有技术中的上述缺陷,是非常有必要的。This is the weak point of prior art. Therefore, aiming at the above-mentioned defects in the prior art, it is very necessary to provide and design a neural network-based soft sensor method and system for copper ore flotation machines to solve the above-mentioned defects in the prior art.
发明内容Contents of the invention
本发明的目的在于,针对上述现有技术存在的缺陷,提供设计一种面向铜矿浮选机的基于神经网络的软测量方法及系统,以解决上述技术问题。The purpose of the present invention is to provide and design a neural network-based soft sensor method and system for copper ore flotation machines to solve the above-mentioned technical problems.
为实现上述目的,本发明给出以下技术方案:To achieve the above object, the present invention provides the following technical solutions:
一种面向铜矿浮选机的基于神经网络的软测量方法,其特征在于,包括以下步骤:A neural network-based soft sensing method for copper ore flotation machines, characterized in that it comprises the following steps:
S1:NNG-RBF神经网络学习算法步骤,基于K-均值聚类方法求取基函数中心c,具体步骤如下:S1: NNG-RBF neural network learning algorithm steps, based on the K-means clustering method to obtain the basis function center c, the specific steps are as follows:
网络初始化:随机选取h个样本作为聚类中心ci(i=1,2,…,h);Network initialization: randomly select h samples as cluster centers c i (i=1, 2, ..., h);
将输入的训练样本集合按最近邻规则分组:按照xp与中心ci之间的欧式距离将xp分配到输入样本的各个聚类集合vp(p=1,2,…P)中;Group the input training sample set according to the nearest neighbor rule: according to the Euclidean distance between x p and the center ci , assign x p to each cluster set v p (p=1, 2, ... P) of the input sample;
xp为第p个输入样本,p=1,2,3,·······,P;P为样本总数,也就是神经网络的训练样本;x p is the pth input sample, p=1, 2, 3,..., P; P is the total number of samples, that is, the training samples of the neural network;
重新调整聚类中心:计算各个聚类集合vp中训练样本的平均值,即新的聚类中心ci,如果新的聚类中心不再发生变化,则所得到的ci即为NNG-RBF神经网络最终的基函数中心,否则再进行将输入的训练样本集合按最近邻规则分组,进行下一轮的中心求解;Readjust the cluster center: calculate the average value of the training samples in each cluster set v p , that is, the new cluster center ci, if the new cluster center does not change anymore , the obtained ci is NNG- The final basis function center of the RBF neural network, otherwise, the input training sample set is grouped according to the nearest neighbor rule, and the next round of center solution is performed;
求解方差σi,所述NNG-RBF神经网络的函数的基函数为高斯函数,方差σi可如下求解:Solve the variance σ i , the base function of the function of the NNG-RBF neural network is a Gaussian function, and the variance σ i can be solved as follows:
式中,cmax是所选取中心之间的最大距离;where c max is the maximum distance between the selected centers;
计算隐含层与输出层之间的权值,隐含层至输出层之间神经元的连接权值可以用最小二乘法直接计算得到:Calculate the weight between the hidden layer and the output layer, and the connection weight of neurons between the hidden layer and the output layer can be directly calculated by the least square method:
进一步,将该RBF神经网络,结合Nonnegative garrote(NNG)算法,通过增加新的系数β(β1,β2,...,βp)重新制定计算公式,利用新增系数β(β1,β2,...,βp)压缩输入变量:Further, the RBF neural network is combined with the Nonnegative garrote (NNG) algorithm, and the calculation formula is re-formulated by adding new coefficients β(β 1 , β 2 ,..., β p ), and the new coefficient β(β 1 , β 2 ,..., β p ) Squeeze the input variables:
此算法可以解决二次非线性约束的问题,对于最优s的选择可以用V折交叉验证,数据集L=X,Y被划分为V个子集,在约束条件下对{βi}极小化:This algorithm can solve the problem of quadratic nonlinear constraints. For the selection of the optimal s, V-fold cross-validation can be used. The data set L=X, Y is divided into V subsets. Next, minimize {β i }:
并将作为新的输入变量权重系数;βi的取值取决于s,s为额外加入的NNG算法参数;βi的大小反映了对应辅助变量对预测模型的重要性。例如,如果βi=0,则说明对应的变量xi对目标函数没有任何影响,从而xi就会被剔除。如果βi=1则对应变量无变化的保留下来。如果0<βi<1则说明相应的变量系数被压缩,也就是变量对于预测模型的作用被压缩了。通过减小s,使更多的{βi}变为零,从而达到变量压缩的目的,这种方法就是NNG-RBF算法。and will As a new input variable weight coefficient; the value of β i depends on s, and s is an additional NNG algorithm parameter added; the size of β i reflects the importance of the corresponding auxiliary variable to the prediction model. For example, if β i =0, it means that the corresponding variable xi has no influence on the objective function, so xi will be eliminated. If β i =1 then the corresponding variable remains unchanged. If 0<β i <1, it means that the corresponding variable coefficient is compressed, that is, the effect of the variable on the prediction model is compressed. By reducing s, more {β i } become zero, so as to achieve the purpose of variable compression, this method is the NNG-RBF algorithm.
S2:最优NNG算法参数的选择及误差预测S2: Selection of optimal NNG algorithm parameters and error prediction
变量选择的目的就是要找到对y影响较大的辅助变量,由辅助变量对y可能出现的情况进行预测。建模精度评价指标:采用均方误差(MSE)评估模型预测精度。数学公式表示为:The purpose of variable selection is to find auxiliary variables that have a greater impact on y, and the auxiliary variables can predict the possible occurrence of y. Modeling accuracy evaluation index: The mean square error (MSE) is used to evaluate the prediction accuracy of the model. The mathematical formula is expressed as:
v-fold交叉验证法首先是把数据集平均分为V份,每次从V份数据集中拿出一份数据集作为验证集,剩下的V-1份数据集作为训练集,重复进行V次,最后平均V次的结果作为最后泛化误差的估计。通常V的取值为5到10时能得到较好的结果,当V取值太大时,方差就会随之增大;当V取值较小时由于参与训练的样本数据减少会导致预测误差的增大;公式如下:The v-fold cross-validation method first divides the data set into V parts on average, each time a data set is taken from the V data set as the verification set, and the remaining V-1 data sets are used as the training set, and V is repeated. times, and the final average of the results of V times is used as an estimate of the final generalization error. Usually, better results can be obtained when the value of V is 5 to 10. When the value of V is too large, the variance will increase accordingly; when the value of V is small, the prediction error will be caused by the reduction of sample data participating in the training. The increase; the formula is as follows:
通过该公式选择最优的NNG算法参数s,并将s值代入公式(4)求解,得到系统最优压缩系数β*。The optimal NNG algorithm parameter s is selected through this formula, and the value of s is substituted into formula (4) to solve, and the optimal compression coefficient β * of the system is obtained.
S3:NNG-RBF算法建模S3: NNG-RBF Algorithm Modeling
通过v-fold交叉验证法对数据进行处理后,得到的s即为训练得到的参数,把s带入到公式中,计算出βi的值。βi的大小反映了对应辅助变量对预测模型的重要性,通过βi的值剔除对预测模型没有任何影响的变量,选取最优变量,从而起到对变量系数压缩的目的。把输入变量带入到已训练好的神经网络中建模预测。After the data is processed through the v-fold cross-validation method, the obtained s is the parameter obtained from the training, and the s is brought into the formula to calculate the value of β i . The size of β i reflects the importance of the corresponding auxiliary variables to the forecasting model, and the variables that have no influence on the forecasting model are eliminated through the value of β i , and the optimal variable is selected, so as to achieve the purpose of compressing the variable coefficients. Bring the input variables into the trained neural network to model predictions.
一种面向铜矿浮选机的基于神经网络的软测量系统,其特征在于,它包括电源模块、主控模块和通信模块,所述的主控模块连接所述的电源模块和通信模块,所述的通信模块还连接有现场采集模块和上位机模块;A neural network-based soft sensor system for copper ore flotation machines is characterized in that it includes a power supply module, a main control module and a communication module, and the main control module is connected to the power supply module and the communication module. The communication module described above is also connected with a field acquisition module and a host computer module;
所述的主控模块内集成有上述NNG-RBF算法的数学建模模块。The above-mentioned mathematical modeling module of the NNG-RBF algorithm is integrated in the main control module.
所述的现场采集模块包括有:Described on-the-spot acquisition module comprises:
SWINGWIRL II电容式涡街流量传感器;SWINGWIRL II电容式涡街流量传感器是采用差动开关电容(DSC)作为检测元件来感测旋涡发生体产生的漩涡频率的一种器材。其优点是工作温度范围很宽,从-200℃~+400℃,抗振性能特别好。同时还具有以下特点:无可动件,测量范围可达40:1,压力损失小,测量准确度较高等。可用于测量封闭管道中气体、蒸汽和液体流量。本装置SWINGWIRL II电容式涡街流量传感器所使用的公称通径为300mm,空气测量范围为1655m3/h-19330m3/h。该流量传感器用于测量装置中的空气充气量的设定值等,基于SWINGWIRL II电容式涡街流量传感器的检测电路如图2所示。SWINGWIRL II capacitive vortex flow sensor; SWINGWIRL II capacitive vortex flow sensor is a device that uses a differential switched capacitor (DSC) as a detection element to sense the vortex frequency generated by the vortex generator. Its advantage is that the working temperature range is very wide, from -200°C to +400°C, and the anti-vibration performance is particularly good. At the same time, it also has the following characteristics: no moving parts, the measurement range can reach 40:1, the pressure loss is small, and the measurement accuracy is high. It can be used to measure gas, steam and liquid flow in closed pipelines. The nominal diameter used by the SWINGWIRL II capacitive vortex flow sensor of this device is 300mm, and the air measurement range is 1655m 3 /h-19330m 3 /h. The flow sensor is used to measure the set value of the air charge in the device, etc. The detection circuit based on the SWINGWIRL II capacitive vortex flow sensor is shown in Figure 2.
CLHGM-2型轮辐式称重传感器;CLHGM-2型轮辐式称重传感器是利用电阻应变原理构成的,弹性体采用比较先进的轮辐式结构形式。电阻式应变片贴在轮辐的中性面上,组成电桥的测量回路。通常情况下,电桥处于平衡状态,桥路无输出当传感器受到外力作用时,轮辐产生相应的变形,电阻应变片阻值发生变化,使桥路失去平衡。在外界供桥电压作用下,电桥输出不平衡电压信号。该信号大小与外力成正比。CLHGM-2型轮辐式称重传感器输出阻抗400Ω,输入阻抗460Ω,可工作的温度范围-20℃~80℃,在各种工矿企业系统中作力的测量分析。该轮辐式称重传感器用于测量装置中给矿总量、石灰添加总量等,基于CLHGM-2型轮辐式称重传感器检测电路如图3所示。CLHGM-2 spoke type load cell; CLHGM-2 spoke type load cell is composed of the principle of resistance strain, and the elastic body adopts a relatively advanced spoke structure. Resistive strain gauges are attached to the neutral plane of the spokes to form the measuring circuit of the bridge. Normally, the bridge is in a balanced state, and the bridge has no output. When the sensor is subjected to external force, the spokes will deform accordingly, and the resistance value of the strain gauge will change, making the bridge out of balance. Under the action of the external bridge voltage, the bridge outputs an unbalanced voltage signal. The magnitude of this signal is proportional to the external force. CLHGM-2 spoke load cell has an output impedance of 400Ω, an input impedance of 460Ω, and a working temperature range of -20°C to 80°C. It is used for force measurement and analysis in various industrial and mining enterprise systems. The spoke-type weighing sensor is used to measure the total amount of ore feeding and the total amount of lime added in the measuring device. The detection circuit based on the CLHGM-2 type spoke-type weighing sensor is shown in Figure 3.
TCD128C-CCD图像传感器;TCD128C-CCD图像传感器是一种能进行光电转换存储信息及转换信息电荷功能的器件。PN结光敏二极管和CCD(电荷耦合器件)构成若干像素的一元光敏二极管阵列,物体通过光学镜头在这种阵列上形成实像。每个光敏元件(像素)呈现不同强度的弱电流,由扫描电路拾取图像信号,在经过处理可获得视频信号。TCD128C-CCD图像传感器优点是自扫描、高灵敏、低噪声、长寿命、低功耗、高可靠。其像元尺寸小,几何精度高,配置适当的光学系统,可获得很高的空间分辨率,使用方便灵活,适应性强,输出信号易于数字化处理,容易与计算机连接组成自动测量控制。有效像素数目1728,有效读取长度210mm。该CD128C-CCD图像传感器用于测量装置中大泡的面积、中泡的面积等,基于TCD128C-CCD图像传感器放大成像测量电路如图4所示。TCD128C-CCD image sensor; TCD128C-CCD image sensor is a device that can perform photoelectric conversion to store information and convert information charges. The PN junction photodiode and CCD (Charge Coupled Device) constitute a unitary photodiode array of several pixels, and the object forms a real image on this array through an optical lens. Each photosensitive element (pixel) presents a weak current of different intensity, the image signal is picked up by the scanning circuit, and the video signal can be obtained after processing. The advantages of TCD128C-CCD image sensor are self-scanning, high sensitivity, low noise, long life, low power consumption and high reliability. Its pixel size is small, the geometric precision is high, and the appropriate optical system can obtain high spatial resolution. It is convenient and flexible to use, and has strong adaptability. The output signal is easy to be digitally processed, and it is easy to connect with a computer to form an automatic measurement control. The number of effective pixels is 1728, and the effective reading length is 210mm. The CD128C-CCD image sensor is used to measure the area of large bubbles and medium bubbles in the device, and the enlarged imaging measurement circuit based on the TCD128C-CCD image sensor is shown in Figure 4.
电源模块在给测量装置供电的同时,同时能起到稳压、保护芯片的作用。主控模块接收数据,然后输入建好的模型从而输出软测量结果。通信模块是接收现场采集的数据,并向上位机发送软测量结果。The power module can not only supply power to the measurement device, but also stabilize the voltage and protect the chip. The main control module receives the data, and then inputs the built model to output the soft measurement result. The communication module receives the data collected on site and sends the soft measurement results to the host computer.
所述的主控模块为基于STM32F103的嵌入式系统,该芯片能工作于-40~105℃的温度范围,能够适应恶劣的工业生产环境。MAX232芯片用于串行口的电平变换,实现控制器与通信接口之间的通信。STM32F103主控芯片如图5所示。主控模块的主控程序流程如图6所示。The main control module is an embedded system based on STM32F103, the chip can work in the temperature range of -40-105°C, and can adapt to harsh industrial production environments. The MAX232 chip is used for the level conversion of the serial port to realize the communication between the controller and the communication interface. The STM32F103 main control chip is shown in Figure 5. The main control program flow of the main control module is shown in Figure 6.
本发明的有益效果在于,把铜矿浮选机装置中难以测量的PH值,通过对装置中18个可测输入变量的筛选,再对数据进行处理,然后利用所筛选的变量建模对PH值进行预测。The beneficial effects of the present invention are that the pH value that is difficult to measure in the copper ore flotation machine device is screened by 18 measurable input variables in the device, and then the data is processed, and then the pH value is analyzed by using the screened variable modeling. value to predict.
对辅助变量的选择是基于NNG-RBF的变量选择方法,然后利用NNG-RBF算法建模和软测量仪表的实时在线校正来保证PH值的预测精度;这种方法具有响应迅速、投资低、维护保养简单等优点。The selection of auxiliary variables is based on the variable selection method of NNG-RBF, and then the NNG-RBF algorithm modeling and real-time online correction of soft measuring instruments are used to ensure the prediction accuracy of the pH value; this method has the advantages of rapid response, low investment, and maintenance Simple maintenance and other advantages.
辅助变量的选择要通过对铜矿浮选机装置的机理分析以及工艺流程来初步确定影响主导变量的相关辅助变量,包括变量类型、变量数目和监测点的选择。实践证明,基于NNG-RBF的变量选择算法可以选择出最佳的辅助变量,进而提高我们的预测精度和降低计算成本。The selection of auxiliary variables should preliminarily determine the relevant auxiliary variables that affect the leading variables through the mechanism analysis of the copper ore flotation machine and the process flow, including the selection of variable types, variable numbers and monitoring points. Practice has proved that the variable selection algorithm based on NNG-RBF can select the best auxiliary variables, thereby improving our prediction accuracy and reducing computing costs.
此外,本发明设计原理可靠,结构简单,具有非常广泛的应用前景。In addition, the design principle of the present invention is reliable, the structure is simple, and has very wide application prospects.
由此可见,本发明与现有技术相比,具有突出的实质性特点和显著地进步,其实施的有益效果也是显而易见的。It can be seen that, compared with the prior art, the present invention has outstanding substantive features and remarkable progress, and the beneficial effects of its implementation are also obvious.
附图说明Description of drawings
表1为铜矿浮选机装置的18个可测输入变量。Table 1 shows the 18 measurable input variables of the copper ore flotation machine.
图1为铜矿浮选机装置矿化过程分区图。Figure 1 is a zoning diagram of the mineralization process of the copper ore flotation machine device.
图2为基于SWINGWIRL II电容式涡街流量传感器的检测电路。Figure 2 is the detection circuit based on SWINGWIRL II capacitive vortex flow sensor.
图3为基于CLHGM-2型轮辐式称重传感器检测电路。Figure 3 is based on the CLHGM-2 type spoke load cell detection circuit.
图4为基于TCD128C-CCD图像传感器放大成像测量电路。Figure 4 is a zoom-in imaging measurement circuit based on the TCD128C-CCD image sensor.
图5为STM32F103主控芯片。Figure 5 shows the STM32F103 main control chip.
图6为主控模块的主控程序流程。Figure 6 is the main control program flow of the main control module.
具体实施方式Detailed ways
下面结合附图并通过具体实施例对本发明进行详细阐述,以下实施例是对本发明的解释,而本发明并不局限于以下实施方式。The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following embodiments.
实施例1:Example 1:
表1为铜矿浮选机装置的18个可测输入变量。Table 1 shows the 18 measurable input variables of the copper ore flotation machine.
表1铜矿浮选机装置的可测输入变量表Table 1 Measurable input variable table of copper ore flotation machine device
本发明提供的一种面向铜矿浮选机的基于神经网络的软测量方法,其特征在于,包括以下步骤:A kind of neural network-based soft sensing method for copper ore flotation machine provided by the invention is characterized in that, comprises the following steps:
S1:NNG-RBF神经网络学习算法步骤,基于K-均值聚类方法求取基函数中心c,具体步骤如下:S1: NNG-RBF neural network learning algorithm steps, based on the K-means clustering method to obtain the basis function center c, the specific steps are as follows:
网络初始化:随机选取h个样本作为聚类中心ci(i=1,2,…,h);Network initialization: randomly select h samples as cluster centers c i (i=1, 2, ..., h);
将输入的训练样本集合按最近邻规则分组:按照xp与中心ci之间的欧式距离将xp分配到输入样本的各个聚类集合vp(p=1,2,…P)中;Group the input training sample set according to the nearest neighbor rule: according to the Euclidean distance between x p and the center ci , assign x p to each cluster set v p (p=1, 2, ... P) of the input sample;
重新调整聚类中心:计算各个聚类集合vp中训练样本的平均值,即新的聚类中心ci,如果新的聚类中心不再发生变化,则所得到的ci即为NNG-RBF神经网络最终的基函数中心,否则再进行将输入的训练样本集合按最近邻规则分组,进行下一轮的中心求解;Readjust the cluster center: calculate the average value of the training samples in each cluster set v p , that is, the new cluster center ci, if the new cluster center does not change anymore , the obtained ci is NNG- The final basis function center of the RBF neural network, otherwise, the input training sample set is grouped according to the nearest neighbor rule, and the next round of center solution is performed;
求解方差σi,所述NNG-RBF神经网络的函数的基函数为高斯函数,方差σi可如下求解:Solve the variance σ i , the base function of the function of the NNG-RBF neural network is a Gaussian function, and the variance σ i can be solved as follows:
式中,cmax是所选取中心之间的最大距离;where c max is the maximum distance between the selected centers;
计算隐含层与输出层之间的权值,隐含层至输出层之间神经元的连接权值可以用最小二乘法直接计算得到:Calculate the weight between the hidden layer and the output layer, and the connection weight of neurons between the hidden layer and the output layer can be directly calculated by the least square method:
进一步,将该RBF神经网络,结合Nonnegative garrote(NNG)算法,通过增加新的系数β(β1,β2,...,βp)重新制定计算公式,利用新增系数β(β1,β2,...,βp)压缩输入变量:Further, the RBF neural network is combined with the Nonnegative garrote (NNG) algorithm, and the calculation formula is re-formulated by adding new coefficients β(β 1 , β 2 ,..., β p ), and the new coefficient β(β 1 , β 2 ,..., β p ) Squeeze the input variables:
此算法可以解决二次非线性约束的问题,对于最优s的选择可以用V折交叉验证,数据集L=X,Y被划分为V个子集,在约束条件下对{βi}极小化:This algorithm can solve the problem of quadratic nonlinear constraints. For the selection of the optimal s, V-fold cross-validation can be used. The data set L=X, Y is divided into V subsets. Next, minimize {β i }:
并将作为新的输入变量权重系数;βi的取值取决于s,s为额外加入的NNG算法参数;βi的大小反映了对应辅助变量对预测模型的重要性。例如,如果βi=0,则说明对应的变量xi对目标函数没有任何影响,从而xi就会被剔除。如果βi=1则对应变量无变化的保留下来。如果0<βi<1则说明相应的变量系数被压缩,也就是变量对于预测模型的作用被压缩了。通过减小s,使更多的{βi}变为零,从而达到变量压缩的目的,这种方法就是NNG-RBF算法。and will As a new input variable weight coefficient; the value of β i depends on s, and s is an additional NNG algorithm parameter added; the size of β i reflects the importance of the corresponding auxiliary variable to the prediction model. For example, if β i =0, it means that the corresponding variable xi has no influence on the objective function, so xi will be eliminated. If β i =1 then the corresponding variable remains unchanged. If 0<β i <1, it means that the corresponding variable coefficient is compressed, that is, the effect of the variable on the prediction model is compressed. By reducing s, more {β i } become zero, so as to achieve the purpose of variable compression, this method is the NNG-RBF algorithm.
S2:最优NNG算法参数的选择及误差预测S2: Selection of optimal NNG algorithm parameters and error prediction
变量选择的目的就是要找到对y影响较大的辅助变量,由辅助变量对y可能出现的情况进行预测。建模精度评价指标:采用均方误差(MSE)评估模型预测精度。数学公式表示为:The purpose of variable selection is to find auxiliary variables that have a greater impact on y, and the auxiliary variables can predict the possible occurrence of y. Modeling accuracy evaluation index: The mean square error (MSE) is used to evaluate the prediction accuracy of the model. The mathematical formula is expressed as:
v-fold交叉验证法首先是把数据集平均分为V份,每次从V份数据集中拿出一份数据集作为验证集,剩下的V-1份数据集作为训练集,重复进行V次,最后平均V次的结果作为最后泛化误差的估计。通常V的取值为5到10时能得到较好的结果,当V取值太大时,方差就会随之增大;当V取值较小时由于参与训练的样本数据减少会导致预测误差的增大;公式如下:The v-fold cross-validation method first divides the data set into V parts on average, each time a data set is taken from the V data set as the verification set, and the remaining V-1 data sets are used as the training set, and V is repeated. times, and the final average of the results of V times is used as an estimate of the final generalization error. Usually, better results can be obtained when the value of V is 5 to 10. When the value of V is too large, the variance will increase accordingly; when the value of V is small, the prediction error will be caused by the reduction of sample data participating in the training. The increase; the formula is as follows:
通过该公式选择最优的NNG算法参数s,并将s值代入公式(4)求解,得到系统最优压缩系数β*。The optimal NNG algorithm parameter s is selected through this formula, and the value of s is substituted into formula (4) to solve, and the optimal compression coefficient β * of the system is obtained.
S3:NNG-RBF算法建模S3: NNG-RBF Algorithm Modeling
通过v-fold交叉验证法对数据进行处理后,得到的s即为训练得到的参数,把s带入到公式中,计算出βi的值。βi的大小反映了对应辅助变量对预测模型的重要性,通过βi的值剔除对预测模型没有任何影响的变量,选取最优变量,从而起到对变量系数压缩的目的。把输入变量带入到已训练好的神经网络中建模预测。After the data is processed through the v-fold cross-validation method, the obtained s is the parameter obtained from the training, and the s is brought into the formula to calculate the value of β i . The size of β i reflects the importance of the corresponding auxiliary variables to the forecasting model, and the variables that have no influence on the forecasting model are eliminated through the value of β i , and the optimal variable is selected, so as to achieve the purpose of compressing the variable coefficients. Bring the input variables into the trained neural network to model predictions.
实施例2:Example 2:
如图2-6所示,本实施提供的一种面向铜矿浮选机的基于神经网络的软测量系统,其特征在于,它包括电源模块、主控模块和通信模块,所述的主控模块连接所述的电源模块和通信模块,所述的通信模块还连接有现场采集模块和上位机模块;As shown in Figure 2-6, a kind of neural network-based soft sensor system oriented to copper ore flotation machine provided by this implementation is characterized in that it includes a power supply module, a main control module and a communication module, and the main control The module is connected to the power supply module and the communication module, and the communication module is also connected to the on-site acquisition module and the host computer module;
所述的主控模块内集成有上述NNG-RBF算法的数学建模模块。The above-mentioned mathematical modeling module of the NNG-RBF algorithm is integrated in the main control module.
所述的现场采集模块包括有:Described on-the-spot acquisition module comprises:
SWINGWIRL II电容式涡街流量传感器;SWINGWIRL II电容式涡街流量传感器是采用差动开关电容(DSC)作为检测元件来感测旋涡发生体产生的漩涡频率的一种器材。其优点是工作温度范围很宽,从-200℃~+400℃,抗振性能特别好。同时还具有以下特点:无可动件,测量范围可达40∶1,压力损失小,测量准确度较高等。可用于测量封闭管道中气体、蒸汽和液体流量。本装置SWINGWIRL II电容式涡街流量传感器所使用的公称通径为300mm,空气测量范围为1655m3/h-19330m3/h。该流量传感器用于测量装置中的空气充气量的设定值等,基于SWINGWIRL II电容式涡街流量传感器的检测电路如图2所示。SWINGWIRL II capacitive vortex flow sensor; SWINGWIRL II capacitive vortex flow sensor is a device that uses a differential switched capacitor (DSC) as a detection element to sense the vortex frequency generated by the vortex generator. Its advantage is that the working temperature range is very wide, from -200°C to +400°C, and the anti-vibration performance is particularly good. At the same time, it also has the following characteristics: no moving parts, the measurement range can reach 40:1, the pressure loss is small, and the measurement accuracy is high. It can be used to measure gas, steam and liquid flow in closed pipelines. The nominal diameter used by the SWINGWIRL II capacitive vortex flow sensor of this device is 300mm, and the air measurement range is 1655m 3 /h-19330m 3 /h. The flow sensor is used to measure the set value of the air charge in the device, etc. The detection circuit based on the SWINGWIRL II capacitive vortex flow sensor is shown in Figure 2.
CLHGM-2型轮辐式称重传感器;CLHGM-2型轮辐式称重传感器是利用电阻应变原理构成的,弹性体采用比较先进的轮辐式结构形式。电阻式应变片贴在轮辐的中性面上,组成电桥的测量回路。通常情况下,电桥处于平衡状态,桥路无输出当传感器受到外力作用时,轮辐产生相应的变形,电阻应变片阻值发生变化,使桥路失去平衡。在外界供桥电压作用下,电桥输出不平衡电压信号。该信号大小与外力成正比。CLHGM-2型轮辐式称重传感器输出阻抗400Ω,输入阻抗460Ω,可工作的温度范围-20℃~80℃,在各种工矿企业系统中作力的测量分析。该轮辐式称重传感器用于测量装置中给矿总量、石灰添加总量等,基于CLHGM-2型轮辐式称重传感器检测电路如图3所示。CLHGM-2 spoke type load cell; CLHGM-2 spoke type load cell is composed of the principle of resistance strain, and the elastic body adopts a relatively advanced spoke structure. Resistive strain gauges are attached to the neutral plane of the spokes to form the measuring circuit of the bridge. Normally, the bridge is in a balanced state, and the bridge has no output. When the sensor is subjected to external force, the spokes will deform accordingly, and the resistance value of the strain gauge will change, making the bridge out of balance. Under the action of the external bridge voltage, the bridge outputs an unbalanced voltage signal. The magnitude of this signal is proportional to the external force. CLHGM-2 spoke load cell has an output impedance of 400Ω, an input impedance of 460Ω, and a working temperature range of -20°C to 80°C. It is used for force measurement and analysis in various industrial and mining enterprise systems. The spoke-type weighing sensor is used to measure the total amount of ore feeding and the total amount of lime added in the measuring device. The detection circuit based on the CLHGM-2 type spoke-type weighing sensor is shown in Figure 3.
TCD128C-CCD图像传感器;TCD128C-CCD图像传感器是一种能进行光电转换存储信息及转换信息电荷功能的器件。PN结光敏二极管和CCD(电荷耦合器件)构成若干像素的一元光敏二极管阵列,物体通过光学镜头在这种阵列上形成实像。每个光敏元件(像素)呈现不同强度的弱电流,由扫描电路拾取图像信号,在经过处理可获得视频信号。TCD128C-CCD图像传感器优点是自扫描、高灵敏、低噪声、长寿命、低功耗、高可靠。其像元尺寸小,几何精度高,配置适当的光学系统,可获得很高的空间分辨率,使用方便灵活,适应性强,输出信号易于数字化处理,容易与计算机连接组成自动测量控制。有效像素数目1728,有效读取长度210mm。该TCD128C-CCD图像传感器用于测量装置中大泡的面积、中泡的面积等,基于TCD128C-CCD图像传感器放大成像测量电路如图4所示。TCD128C-CCD image sensor; TCD128C-CCD image sensor is a device that can perform photoelectric conversion to store information and convert information charges. The PN junction photodiode and CCD (Charge Coupled Device) constitute a unitary photodiode array of several pixels, and the object forms a real image on this array through an optical lens. Each photosensitive element (pixel) presents a weak current of different intensity, the image signal is picked up by the scanning circuit, and the video signal can be obtained after processing. The advantages of TCD128C-CCD image sensor are self-scanning, high sensitivity, low noise, long life, low power consumption and high reliability. Its pixel size is small, the geometric precision is high, and the appropriate optical system can obtain high spatial resolution. It is convenient and flexible to use, and has strong adaptability. The output signal is easy to be digitally processed, and it is easy to connect with a computer to form an automatic measurement control. The number of effective pixels is 1728, and the effective reading length is 210mm. The TCD128C-CCD image sensor is used to measure the area of large bubbles and medium bubbles in the device, and the enlarged imaging measurement circuit based on the TCD128C-CCD image sensor is shown in Figure 4.
电源模块在给测量装置供电的同时,同时能起到稳压、保护芯片的作用。主控模块接收数据,然后输入建好的模型从而输出软测量结果。通信模块是接收现场采集的数据,并向上位机发送软测量结果。The power module can not only supply power to the measurement device, but also stabilize the voltage and protect the chip. The main control module receives the data, and then inputs the built model to output the soft measurement result. The communication module receives the data collected on site and sends the soft measurement results to the host computer.
所述的主控模块为基于STM32F103的嵌入式系统,该芯片能工作于-40~105℃的温度范围,能够适应恶劣的工业生产环境。MAX232芯片用于串行口的电平变换,实现控制器与通信接口之间的通信。STM32F103主控芯片如图5所示。主控模块的主控程序流程如图6所示。The main control module is an embedded system based on STM32F103, the chip can work in the temperature range of -40-105°C, and can adapt to harsh industrial production environments. The MAX232 chip is used for the level conversion of the serial port to realize the communication between the controller and the communication interface. The STM32F103 main control chip is shown in Figure 5. The main control program flow of the main control module is shown in Figure 6.
以上公开的仅为本发明的优选实施方式,但本发明并非局限于此,任何本领域的技术人员能思之的没有创造性的变化,以及在不脱离本发明原理前提下所作的若干改进和润饰,都应落在本发明的保护范围内。The above disclosure is only the preferred embodiment of the present invention, but the present invention is not limited thereto, any non-creative changes that those skilled in the art can think of, and some improvements and modifications made without departing from the principle of the present invention , should fall within the protection scope of the present invention.
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CN110672792A (en) * | 2019-10-09 | 2020-01-10 | 中南大学 | Soft measurement method and system for pH value in neutral leaching process of zinc hydrometallurgy |
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CN111141341A (en) * | 2019-12-31 | 2020-05-12 | 华东理工大学 | Compensation method for turbine flowmeter, system and storage medium thereof |
CN111482280A (en) * | 2020-04-22 | 2020-08-04 | 齐鲁工业大学 | A method and system for intelligent soft sensing of copper mine flotation based on wireless sensor network |
CN111983140A (en) * | 2020-07-21 | 2020-11-24 | 齐鲁工业大学 | Carbon monoxide measuring system and method for dry quenching production |
CN111983140B (en) * | 2020-07-21 | 2022-05-10 | 齐鲁工业大学 | Carbon monoxide measuring system and method for dry quenching production |
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