本文選題：模糊網(wǎng)絡 + 機翼變形測量　； 參考：《西安電子科技大學》2015年碩士論文

【摘要】：高空長航時無人機的發(fā)展日益受到重視,而對其機翼長基線天線變形的實時高精度測量對保證天線性能具有重要意義。本文將模糊理論應用到機翼變形測量的實際問題中,基于Takagi-Sugeno-Kang(TSK)型模糊邏輯系統(tǒng)的普遍逼近特性,提出兩種不基于被測對象的新型模糊網(wǎng)絡算法,使其能夠更為準確有效地逼近應變量和形變位移量二者之間的關系,具有較強的推理能力、自適應學習能力和通用性,為機翼變形測量提供一種新的思路。本文首先針對自構(gòu)架模糊網(wǎng)絡法(Self-Structuring Fuzzy Network,SSFN)抗干擾性較差的缺點,結(jié)合神經(jīng)網(wǎng)絡和區(qū)間二型模糊理論,提出一種新型自構(gòu)架區(qū)間二型模糊神經(jīng)網(wǎng)絡(Self-Structuring Interval Type-2 Fuzzy Neural Network,SSIT2FNN)。在SSIT2FNN中,規(guī)則前件為區(qū)間二型模糊集合,用于將每條規(guī)則的激活強度反饋到自身構(gòu)成內(nèi)反饋回路,其參數(shù)學習采用梯度下降算法;后件為帶有區(qū)間權值的TSK模型,其參數(shù)學習采用有序規(guī)則卡爾曼濾波算法。網(wǎng)絡的初始規(guī)則數(shù)為零,并且所有規(guī)則均通過結(jié)構(gòu)學習和前后件參數(shù)同時在線學習來產(chǎn)生。由于SSIT2FNN是基于訓練誤差最小化的思路來進行網(wǎng)絡的訓練學習,在網(wǎng)絡的構(gòu)建過程中往往依賴于訓練經(jīng)驗,從而使得系統(tǒng)泛化能力較差。因此,本文又結(jié)合聚類分裂的思想和支持向量回歸(Support Vector Regression,SVR)理論,提出一種自分裂迭代線性支持向量回歸模糊網(wǎng)絡(Self-Splitting Iterative Linear SVR Fuzzy Network,SSILSVRFN)。在SSILSVRFN中,網(wǎng)絡的構(gòu)建過程主要分為結(jié)構(gòu)學習和參數(shù)學習兩個部分,其中,結(jié)構(gòu)學習采用自分裂規(guī)則生成算法(Self-Splitting Rule Generation,SSRG)來自動生成規(guī)則并對其初始化;參數(shù)學習則基于結(jié)構(gòu)風險最小化原則,采用迭代線性SVR(Iterative Linear SVR,ILSVR)的學習算法來對規(guī)則的前、后件參數(shù)進行迭代優(yōu)化。并針對同一非線性函數(shù)進行逼近仿真,深入對比分析了不同方法之間的特點。最后,自主設計機翼框架模型并搭建測量實驗平臺,完成機翼框架模型在不同靜態(tài)載荷下的變形測量實驗。實驗結(jié)果表明,本文提出的兩種改進型模糊網(wǎng)絡算法實現(xiàn)了對逼近精度的逐步提高,能夠較為準確的反映出應變量和形變位移量二者之間的關系,并初步驗證了模糊網(wǎng)絡變形測量方法在實際中的有效性。
[Abstract]:The development of UAV has been paid more and more attention during long altitude navigation, and it is very important to measure the deformation of wing long baseline antenna with high precision in real time to ensure the antenna performance. In this paper, the fuzzy theory is applied to the practical problem of wing deformation measurement. Based on the general approximation property of Takagi-Sugeno-Kangn TSK-based fuzzy logic system, two new fuzzy network algorithms are proposed, which are not based on the measured object. It can more accurately and effectively approach the relationship between strain and deformation displacement. It has strong reasoning ability, adaptive learning ability and generality, which provides a new way for wing deformation measurement. In this paper, first of all, aiming at the disadvantage of the self-Structuring Fuzzy Network (SSFN) method, combining the neural network and the interval type 2 fuzzy theory, a new self-structuring Interval Type-2 Fuzzy Neural network SSIT2FNN is proposed. In SSIT2FNN, the former part of the rule is interval type 2 fuzzy set, which is used to feedback the activation intensity of each rule to the inner feedback loop. The parameter learning adopts gradient descent algorithm, and the latter part is the TSK model with interval weight. The order rule Kalman filter algorithm is used for parameter learning. The number of initial rules of the network is zero, and all the rules are generated by learning the structure and the parameters simultaneously online. Because SSIT2FNN is based on the idea of minimizing the training error, it often relies on the training experience in the process of network construction, which makes the generalization ability of the system poor. Therefore, combining the idea of clustering splitting with the support vector regression support Vector regress (SVR) theory, a self-splitting iterative linear support vector regression fuzzy network called Self-Splitting Iterative Linear SVR Fuzzy Network is proposed in this paper. In SSILSVRFN, the construction process of the network is mainly divided into two parts: structure learning and parameter learning. The self-splitting Rule generation algorithm (SSRG) is used to automatically generate and initialize the rules. Parameter learning is based on the principle of structural risk minimization, and iterative linear SVR(Iterative Linear SVR (ILSVR) learning algorithm is used to optimize the parameters of the first and last parts of the rules. The approximation simulation of the same nonlinear function is carried out, and the characteristics of different methods are compared and analyzed in depth. Finally, the wing frame model is designed and the experimental platform is built to measure the deformation of the wing frame model under different static loads. The experimental results show that the proposed two improved fuzzy network algorithms can improve the approximation accuracy step by step, and can accurately reflect the relationship between strain and deformation displacement. The effectiveness of the fuzzy network deformation measurement method in practice is preliminarily verified.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：V279

【參考文獻】

相關期刊論文 前10條

1 袁慎芳;閆美佳;張巾巾;邱雷;;一種適用于梁式機翼的變形重構(gòu)方法[J];南京航空航天大學學報;2014年06期

2 姚蘭;肖建;王嵩;蔣玉蓮;;自組織區(qū)間二型模糊神經(jīng)網(wǎng)絡及其自適應學習算法[J];控制理論與應用;2013年06期

3 閆好奎;任建國;;電阻應變片的工作原理[J];計量與測試技術;2013年04期

4 周永興;;飛行試驗機翼變形測量的一種方法[J];測控技術;2013年04期

5 王寅;朱振宇;陳志平;賈永清;;一種適于柔性無人機機翼形變的測試方法[J];計算機測量與控制;2012年11期

6 楊國偉;鄭冠男;;基于靜氣動彈性效應的飛機型架外形修正方法研究[J];航空工程進展;2011年02期

7 李巧真;李剛;韓欽澤;;電阻應變片的實驗與應用[J];實驗室研究與探索;2011年04期

8 張琦;李新娥;祖靜;;電阻應變式傳感器的穩(wěn)定性[J];光電技術應用;2009年05期

9 王瑜;喬學光;傅海威;禹大寬;馬超;張晶;;光纖光柵傳感系統(tǒng)信號解調(diào)新技術研究[J];電光與控制;2008年02期

10 姚遠;易本順;肖進勝;;光纖光柵傳感器的波長解調(diào)技術研究進展[J];光通信技術;2007年11期

相關碩士學位論文 前1條

1 李明;柔性機翼長基線天線形變實時測量[D];西安電子科技大學;2015年

，

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