本文選題：聯(lián)合粉磨系統(tǒng)　切入點(diǎn)：磨機(jī)負(fù)荷　出處：《濟(jì)南大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：磨機(jī)是聯(lián)合粉磨系統(tǒng)中的核心設(shè)備,然而大部分磨機(jī)都處于低效率、高耗能的狀態(tài),且粉磨過(guò)程具有高耦合性等特點(diǎn),因此對(duì)磨機(jī)負(fù)荷的準(zhǔn)確辨識(shí)尤為重要。為了得到最優(yōu)的磨機(jī)負(fù)荷模型,分別采用了四種遞推最小二乘算法、RBF神經(jīng)網(wǎng)絡(luò)以及T-S模糊模型對(duì)磨機(jī)負(fù)荷進(jìn)行了辨識(shí)。本課題完成的主要工作概況如下:通過(guò)對(duì)聯(lián)合粉磨系統(tǒng)和磨機(jī)負(fù)荷辨識(shí)發(fā)展現(xiàn)狀的概述,并結(jié)合大量的歷史數(shù)據(jù),得出了磨機(jī)的主電機(jī)電流最能表征磨機(jī)負(fù)荷,以及分析出影響主電機(jī)電流的主要變量有:總量給定、選粉機(jī)轉(zhuǎn)速、循環(huán)風(fēng)機(jī)轉(zhuǎn)速、分料閥開(kāi)度以及粉煤灰?guī)焯嵘龣C(jī)電流。最終確定了總量給定和選粉機(jī)轉(zhuǎn)速對(duì)主電機(jī)電流影響最大,故作為關(guān)鍵變量,其他三個(gè)變量為不確定因素。文中所有的負(fù)荷模型都以總量給定和選粉機(jī)轉(zhuǎn)速作為輸入變量,主電機(jī)電流作為輸出變量。首先采用了四種遞推最小二乘算法對(duì)磨機(jī)負(fù)荷進(jìn)行辨識(shí),具體的算法有:遞推最小二乘法、遺忘因子遞推最小二乘法、限定記憶遞推最小二乘法和偏差補(bǔ)償遞推最小二乘法。通過(guò)仿真結(jié)果分析,得出在該工況下,加入遺忘因子和采用偏差補(bǔ)償策略的模型能很好的跟蹤主電機(jī)電流的變化情況,其中基于偏差補(bǔ)償算法的模型最為精確。基于普通遞推最小二乘和限定記憶的模型擬合誤差相對(duì)較大,不適合該工況下的建模。然后采用了神經(jīng)網(wǎng)絡(luò)對(duì)磨機(jī)負(fù)荷進(jìn)行辨識(shí)。由于RBF神經(jīng)網(wǎng)絡(luò)對(duì)非線性系統(tǒng)具有良好的逼近性能等優(yōu)點(diǎn),故分別采用了基于高斯核函數(shù),多二次核函數(shù)和逆多二次核函數(shù)的三種RBF網(wǎng)絡(luò)模型進(jìn)行了辨識(shí)。徑向基函數(shù)的中心,基寬度以及連接的權(quán)值均采用梯度下降法進(jìn)行訓(xùn)練。神經(jīng)元的個(gè)數(shù)通過(guò)反復(fù)實(shí)驗(yàn)來(lái)確定。通過(guò)分析平均誤差、均方誤差等性能指標(biāo),最終得到基于高斯核函數(shù)的RBF神經(jīng)網(wǎng)絡(luò)模型具有較高的精確度,更適合估計(jì)該工況下的磨機(jī)負(fù)荷。最后采用了T-S模糊模型對(duì)磨機(jī)負(fù)荷進(jìn)行辨識(shí)。利用模糊C-均值聚類(lèi)算法將輸入變量劃分為四個(gè)子空間,并利用加權(quán)最小二乘法對(duì)模糊后件的參數(shù)進(jìn)行了辨識(shí),得到了較為精確的T-S模糊模型。為了對(duì)比文中所用三類(lèi)辨識(shí)方法的有效性和建模精度,在文章的最后分別用最小二乘法中的加權(quán)最小二乘法和基于高斯核函數(shù)的RBF神經(jīng)網(wǎng)絡(luò)對(duì)同一段歷史數(shù)據(jù)進(jìn)行建模。由仿真結(jié)果可知,相比于加權(quán)最小二乘法辨識(shí)出的模型和基于高斯核函數(shù)的RBF神經(jīng)網(wǎng)絡(luò)模型,T-S模糊模型能很好的反映出該段工況下磨機(jī)主電機(jī)電流的變化情況。
[Abstract]:The grinding machine is the core equipment in the combined grinding system. However, most of the grinding machines are in the state of low efficiency and high energy consumption, and the grinding process has the characteristics of high coupling, etc. Therefore, it is very important to identify the mill load accurately. In order to get the optimal load model, Four kinds of recursive least square algorithm (RBF) neural network and T-S fuzzy model are used to identify the load of mill. The main work of this paper is as follows: the development of load identification of combined grinding system and mill is summarized. Combined with a large number of historical data, it is concluded that the main motor current of the mill can best characterize the mill load, and the main variables that affect the main motor current are as follows: the total quantity given, the speed of the separator, the speed of the circulating fan. The opening of the valve and the current of the hoist in the fly ash storehouse are determined to be the most important variables for the main motor because the total quantity and the speed of the separator have the greatest influence on the current of the main motor. The other three variables are uncertain factors. All the load models in this paper take the total quantity given and the speed of the separator as input variables. The main motor current is used as the output variable. Firstly, four recursive least square algorithms are used to identify the mill load. The specific algorithms are: recursive least square method, recursive least square method of forgetting factor, The finite memory recursive least square method and deviation compensation recursive least square method are used. Through the analysis of simulation results, it is concluded that the model with forgetting factor and deviation compensation strategy can track the change of main motor current well. The model based on deviation compensation algorithm is the most accurate, and the model fitting error based on ordinary recursive least squares and limited memory is relatively large. It is not suitable for modeling under this condition. Then, neural network is used to identify mill load. Because RBF neural network has good approximation performance to nonlinear system, it adopts Gao Si kernel function respectively. Three kinds of RBF network models of multi-quadratic kernel function and inverse polyquadratic kernel function are identified. The base width and the weight of connection are trained by gradient descent method. The number of neurons is determined by repeated experiments. The average error, mean square error and other performance indexes are analyzed. Finally, the RBF neural network model based on Gao Si kernel function has high accuracy. Finally, T-S fuzzy model is used to identify the mill load. The input variables are divided into four subspaces by using fuzzy C-means clustering algorithm. By using the weighted least square method, the parameters of the fuzzy rear parts are identified, and a more accurate T-S fuzzy model is obtained. In order to compare the effectiveness and modeling accuracy of the three identification methods used in this paper, At the end of the paper, the weighted least square method and the RBF neural network based on Gao Si kernel function are used to model the same historical data. Compared with the model identified by the weighted least square method and the RBF neural network model based on Gao Si kernel function, the fuzzy model of T-S can well reflect the change of the main motor current under this working condition.
【學(xué)位授予單位】：濟(jì)南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TQ172.63

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 張傳鋒;基于工況識(shí)別的水泥球磨機(jī)負(fù)荷優(yōu)化控制[D];濟(jì)南大學(xué);2012年

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本文編號(hào)：1578797