非線性系統(tǒng)即時學習建模方法研究
發(fā)布時間：2018-10-12 14:50
【摘要】：流程工業(yè)系統(tǒng)具有非線性、強烈時變性和不確定性,單一的全局建模方法往往無法滿足系統(tǒng)建模與優(yōu)化的需求�；诜侄沃枷氲木植拷榻鉀Q這一問題提供了一種有效策略。本文結(jié)合信息熵與樣本的時段特性,提出一種即時學習建模算法。具體工作歸納如下:相似度準則決定了即時學習建模的精度,然而,常用的相似度準則僅考慮樣本之間的相似性,而沒有考慮輸入變量與輸出變量之間的相關(guān)性,從而影響了預(yù)測精度。因此,本文結(jié)合信息熵提出一種改進相似性度量準則的即時學習建模算法。它利用互信息評估輸入變量與輸出變量之間相關(guān)程度,構(gòu)建潛在建�？臻g,并在此潛在空間中定義相似度指標。數(shù)值仿真和在青霉素發(fā)酵過程中的應(yīng)用表明,與傳統(tǒng)的即時學習方法相比,本文所提方法的預(yù)測精度明顯提高。在流程工業(yè)中,過程變量的特征往往隨著時間的變化而發(fā)生變化,亦即:具有明顯的時間特性。而傳統(tǒng)的即時學習方法采用全局搜索策略,忽略了數(shù)據(jù)的時段特性。因此,本文在相似度準則中融入時間信息,提出一種時變系統(tǒng)即時學習建模算法。首先采用聚類算法將數(shù)據(jù)庫樣本劃分為相應(yīng)的幾個時段,其中聚類使用的相似度準則融入時間信息;然后使用分層搜索策略尋找局部建模樣本。在此基礎(chǔ)上,提出局部模型更新策略,利用偏移補償算法矯正模型輸出,降低即時學習的在線計算量。最后,數(shù)值仿真驗證所提算法的有效性。
[Abstract]:Process industry systems are nonlinear, highly time-varying and uncertain. A single global modeling method is often unable to meet the requirements of system modeling and optimization. Local modeling based on divide-and-conquer provides an effective strategy to solve this problem. In this paper, an instant learning modeling algorithm is proposed based on the information entropy and the time characteristics of samples. The specific work is summarized as follows: similarity criterion determines the accuracy of real-time learning modeling. However, the commonly used similarity criteria only consider the similarity between samples, but not the correlation between input variables and output variables. Thus, the prediction accuracy is affected. Therefore, this paper proposes an improved real-time learning modeling algorithm based on information entropy. It uses mutual information to evaluate the correlation between input variable and output variable, constructs the potential modeling space, and defines the similarity index in the potential space. Numerical simulation and application in penicillin fermentation show that the prediction accuracy of the proposed method is much higher than that of the traditional real-time learning method. In the process industry, the characteristics of process variables often change with the change of time, that is, they have obvious time characteristics. The traditional real-time learning method adopts global search strategy and neglects the time characteristic of data. Therefore, this paper presents a real-time learning modeling algorithm for time-varying systems by incorporating time information into similarity criteria. Firstly, the database samples are divided into several periods by clustering algorithm, in which the similarity criterion used in clustering is incorporated into time information, and then the local modeling samples are found by hierarchical search strategy. On this basis, the local model updating strategy is proposed, and the offset compensation algorithm is used to correct the model output, which reduces the on-line computation of real-time learning. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP301.6
本文編號：2266538
[Abstract]:Process industry systems are nonlinear, highly time-varying and uncertain. A single global modeling method is often unable to meet the requirements of system modeling and optimization. Local modeling based on divide-and-conquer provides an effective strategy to solve this problem. In this paper, an instant learning modeling algorithm is proposed based on the information entropy and the time characteristics of samples. The specific work is summarized as follows: similarity criterion determines the accuracy of real-time learning modeling. However, the commonly used similarity criteria only consider the similarity between samples, but not the correlation between input variables and output variables. Thus, the prediction accuracy is affected. Therefore, this paper proposes an improved real-time learning modeling algorithm based on information entropy. It uses mutual information to evaluate the correlation between input variable and output variable, constructs the potential modeling space, and defines the similarity index in the potential space. Numerical simulation and application in penicillin fermentation show that the prediction accuracy of the proposed method is much higher than that of the traditional real-time learning method. In the process industry, the characteristics of process variables often change with the change of time, that is, they have obvious time characteristics. The traditional real-time learning method adopts global search strategy and neglects the time characteristic of data. Therefore, this paper presents a real-time learning modeling algorithm for time-varying systems by incorporating time information into similarity criteria. Firstly, the database samples are divided into several periods by clustering algorithm, in which the similarity criterion used in clustering is incorporated into time information, and then the local modeling samples are found by hierarchical search strategy. On this basis, the local model updating strategy is proposed, and the offset compensation algorithm is used to correct the model output, which reduces the on-line computation of real-time learning. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP301.6
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