本文選題：蜻蜓算法 + 甘蔗收獲機(jī)�。� 參考：《廣西民族大學(xué)》2017年碩士論文

【摘要】：目前,甘蔗機(jī)械化收割過程中存在著堵塞嚴(yán)重、破頭率高、切割質(zhì)量差等問題,進(jìn)而導(dǎo)致來(lái)年甘蔗宿根的發(fā)芽率偏低,極大影響了甘蔗的產(chǎn)量和甘蔗機(jī)械化的推廣。其中,剝?nèi)~系統(tǒng)性能的好壞是影響堵塞的關(guān)鍵所在,而切割器是直接影響甘蔗宿根切割質(zhì)量的關(guān)鍵部件,為實(shí)現(xiàn)對(duì)剝?nèi)~性能的優(yōu)化,以及探究復(fù)雜因素對(duì)刀盤軸向振動(dòng)及切割質(zhì)量的影響規(guī)律并實(shí)現(xiàn)對(duì)切割質(zhì)量的預(yù)測(cè)與控制,本文利用智能優(yōu)化算法的自適應(yīng)性、容錯(cuò)性以及強(qiáng)魯棒性以提高甘蔗收獲機(jī)剝?nèi)~系統(tǒng)的性能和對(duì)刀盤切割系統(tǒng)的切割質(zhì)量預(yù)測(cè)。蜻蜓算法是一種新型群體智能優(yōu)化算法,該算法源于自然界中蜻蜓捕食和遷徙的群體行為,通過模擬蜻蜓群體飛行、捕食、躲避外敵等過程進(jìn)行全局搜索和局部搜索,從而實(shí)現(xiàn)優(yōu)化的目的。該算法具有結(jié)構(gòu)簡(jiǎn)單、易于實(shí)現(xiàn)、搜索性能優(yōu)異且魯棒性強(qiáng)等特點(diǎn),然而在解決一些復(fù)雜的優(yōu)化問題時(shí)存在收斂后期易陷入局部最優(yōu)的缺陷,一定程度上影響了對(duì)甘蔗收獲機(jī)的結(jié)構(gòu)性能的優(yōu)化。為提高甘蔗收獲機(jī)剝?nèi)~系統(tǒng)的性能以及切割質(zhì)量預(yù)測(cè)的精度,本文針對(duì)基本蜻蜓算法存在的不足進(jìn)行分析和改進(jìn),并將改進(jìn)后的算法應(yīng)用于解決甘蔗收獲機(jī)的優(yōu)化問題。本文的主要工作如下:(1)引入精英反向?qū)W習(xí)策略,在保證種群多樣性的同時(shí),擴(kuò)大了搜索區(qū)域的范圍,同時(shí),在迭代中對(duì)蜻蜓個(gè)體的位置更新利用指數(shù)函數(shù)步長(zhǎng)來(lái)代替原始的線性步長(zhǎng),有效提高了算法的收斂速度,從而增強(qiáng)了算法的全局勘探能力和收斂速度。(2)利用上述改進(jìn)后的算法實(shí)現(xiàn)對(duì)剝?nèi)~系統(tǒng)的PID控制器參數(shù)優(yōu)化,解決了傳統(tǒng)PID參數(shù)優(yōu)化方法易出現(xiàn)費(fèi)時(shí)、震蕩且不能保證所調(diào)參數(shù)最優(yōu)的問題,同時(shí)通過PID控制實(shí)現(xiàn)了剝?nèi)~和輸送工序的速度匹配問題,有效緩解了阻塞問題。(3)為解決傳統(tǒng)預(yù)測(cè)方法對(duì)刀盤振動(dòng)預(yù)測(cè)精度低、參數(shù)選取盲目等問題,提出一種基于蜻蜓算法支持向量機(jī)的甘蔗收獲機(jī)刀盤振動(dòng)及性能的預(yù)測(cè)模型。該方法利用蜻蜓群體尋優(yōu)的過程實(shí)現(xiàn)對(duì)支持向量機(jī)參數(shù)的優(yōu)化,并將優(yōu)化后的支持向量機(jī)對(duì)刀盤振動(dòng)進(jìn)行預(yù)測(cè),實(shí)驗(yàn)數(shù)據(jù)表明,基于蜻蜓算法的支持向量機(jī)預(yù)測(cè)模型具有更高的預(yù)測(cè)精度和泛化能力,有效地實(shí)現(xiàn)了對(duì)甘蔗收獲機(jī)刀盤振動(dòng)的預(yù)測(cè)。(4)引入免疫選擇算子,利用免疫選擇操作對(duì)蜻蜓種群進(jìn)行更新,能夠有效抑制算法在收斂過程中易出現(xiàn)的早熟停滯現(xiàn)象,以提高其全局尋優(yōu)能力和尋優(yōu)精度。然后利用優(yōu)化后的蜻蜓算法優(yōu)化支持向量機(jī)的訓(xùn)練參數(shù),并將優(yōu)化后的支持向量機(jī)對(duì)甘蔗收獲機(jī)的宿根切割質(zhì)量進(jìn)行預(yù)測(cè),仿真結(jié)果表明利用改進(jìn)后的蜻蜓算法優(yōu)化的支持向量機(jī)具有更優(yōu)的預(yù)測(cè)性能。
[Abstract]:At present, there are some problems in the process of sugarcane mechanized harvesting, such as serious blockage, high head breaking rate and poor cutting quality, which leads to the low germination rate of sugarcane roots in the coming year, which greatly affects the yield of sugarcane and the popularization of sugarcane mechanization. Among them, the performance of the leaf-stripping system is the key to affect the clogging, and the cutter is the key component that directly affects the cutting quality of sugarcane root. And to explore the influence of complex factors on the axial vibration and cutting quality of the cutter head, and to realize the prediction and control of the cutting quality, this paper makes use of the self-adaptability of the intelligent optimization algorithm. Fault tolerance and strong robustness are used to improve the performance of the leaf stripping system of sugarcane harvester and to predict the cutting quality of the cutter cutting system. Dragonfly algorithm is a new kind of swarm intelligence optimization algorithm, which originates from the swarm behavior of predation and migration of dragonflies in nature. The global search and local search are carried out by simulating the flight, predation and avoidance of foreign enemies of dragonflies. In order to achieve the purpose of optimization. The algorithm has the advantages of simple structure, easy to implement, excellent search performance and strong robustness. However, there are some defects in solving some complex optimization problems, such as the local optimum is easy to fall into in the later stage of convergence. To some extent, it affects the optimization of the structure and performance of sugarcane harvester. In order to improve the performance of leaf stripping system of sugarcane harvester and the precision of cutting quality prediction, this paper analyzes and improves the shortcomings of the basic dragonfly algorithm, and applies the improved algorithm to solve the optimization problem of sugarcane harvester. The main work of this paper is as follows: (1) the elite reverse learning strategy is introduced, which not only ensures the diversity of the population, but also expands the scope of the search area, at the same time, In the iteration, the exponential function step size is used to replace the original linear step to update the position of individual dragonfly, which effectively improves the convergence speed of the algorithm. Thus, the global exploration ability and convergence rate of the algorithm are enhanced. The improved algorithm is used to optimize the parameters of PID controller of the leaf-stripping system, and the traditional PID parameter optimization method is easy to take time. In order to solve the problem of vibration prediction of cutter head by traditional prediction method, the speed matching problem of blade stripping and conveying process is realized by PID control, which effectively alleviates the blockage problem. This paper presents a prediction model of vibration and performance of sugarcane harvester based on dragonfly support vector machine (SVM). In this method, the parameters of support vector machine are optimized by using dragonfly population optimization process, and the optimized support vector machine is used to predict the vibration of cutter head. The experimental data show that, The prediction model of support vector machine based on dragonfly algorithm has higher prediction precision and generalization ability, and the immune selection operator is introduced to predict the vibration of cutter head of sugarcane harvester effectively. Using immune selection to update the dragonfly population can effectively suppress the premature stagnation in the convergence process of the algorithm in order to improve its global optimization ability and optimization accuracy. Then the optimized dragonfly algorithm is used to optimize the training parameters of support vector machine, and the optimized support vector machine is used to predict the cutting quality of sugarcane harvester. The simulation results show that the support vector machine (SVM) optimized by the improved dragonfly algorithm has better prediction performance.
【學(xué)位授予單位】：廣西民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：S225.53;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 吳偉民;吳汪洋;林志毅;李澤熊;方典禹;;基于增強(qiáng)個(gè)體信息交流的蜻蜓算法[J];計(jì)算機(jī)工程與應(yīng)用;2017年04期

2 傅軍棟;陳俐;康水華;馮奕軒;;基于蜻蜓算法和支持向量機(jī)的變壓器故障診斷[J];華東交通大學(xué)學(xué)報(bào);2016年04期

3 馬_g廷;郭敏;;基于極限學(xué)習(xí)與蜻蜓算法的小麥碰撞聲信號(hào)檢測(cè)與識(shí)別[J];電子設(shè)計(jì)工程;2016年05期

4 謝盧鑫;王俊;程紹明;胡金冰;;甘蔗收獲機(jī)剝?nèi)~斷尾系統(tǒng)的設(shè)計(jì)與轉(zhuǎn)速優(yōu)化[J];農(nóng)業(yè)工程學(xué)報(bào);2016年06期

5 蔡超;周武能;;人工蜂群算法整定PID控制器參數(shù)[J];自動(dòng)化儀表;2015年08期

6 王慶喜;儲(chǔ)澤楠;;基于動(dòng)態(tài)布谷鳥搜索算法的PID控制器參數(shù)優(yōu)化[J];計(jì)算機(jī)測(cè)量與控制;2015年04期

7 沈中華;李尚平;麻芳蘭;韋榮發(fā);高建立;;提高整桿式小型甘蔗收割機(jī)喂入能力的試驗(yàn)研究[J];中國(guó)農(nóng)機(jī)化學(xué)報(bào);2015年01期

8 王培崇;高文超;錢旭;茍海燕;汪慎文;;應(yīng)用精英反向?qū)W習(xí)的混合煙花爆炸優(yōu)化算法[J];計(jì)算機(jī)應(yīng)用;2014年10期

9 麻芳蘭;韋榮發(fā);范志達(dá);趙靜;;小型整稈式甘蔗收割機(jī)切割系統(tǒng)的改進(jìn)與試驗(yàn)[J];農(nóng)業(yè)工程學(xué)報(bào);2014年15期

10 周新宇;吳志健;王暉;李康順;張浩宇;;一種精英反向?qū)W習(xí)的粒子群優(yōu)化算法[J];電子學(xué)報(bào);2013年08期

相關(guān)碩士學(xué)位論文 前1條

1 陸聰玲;整桿式甘蔗聯(lián)合收割機(jī)液壓系統(tǒng)設(shè)計(jì)與速度控制研究[D];燕山大學(xué);2015年

，

本文編號(hào)：1831845