基于粒子群算法的高斯過程建模在天線優(yōu)化上的應用研究
發(fā)布時間：2019-03-18 21:23
【摘要】：當對天線進行優(yōu)化設計時,可以結合電磁仿真軟件HFSS和粒子群優(yōu)化算法予以實現(xiàn),但是調用HFSS評估粒子群算法的適應度時需要花費大量的時間,同時也對計算機性能有較高的要求,從而對復雜結構的天線設計造成困難。本文針對多種用途和頻段的天線,將高斯過程建模方法融合到粒子群優(yōu)化算法中,對天線進行了優(yōu)化設計,達到設計指標要求;同時和以往粒子群算法與HFSS軟件相結合的方法進行了比較,證明高斯過程-粒子群聯(lián)合算法在優(yōu)化時間上具有極大的優(yōu)勢。本文主要的研究工作如下:1、介紹了高斯過程模型的建立方法及評估方法,以矩形側饋微帶天線模型為例,表明高斯過程模型預測該天線的頻率特性具有一定的精確性。2、介紹了高斯過程-粒子群聯(lián)合算法的思路,從時間的角度將該方法與調用電磁仿真軟件HFSS作為粒子群算法適應度評價方案的方法進行了對比,說明了高斯過程-粒子群聯(lián)合算法的優(yōu)勢。3、對印刷偶極子天線的頻率特性進行高斯過程建模,解決了有帶寬要求的天線頻率特性建模問題,并將此高斯過程模型融合到粒子群算法中去,對印刷偶極子天線某些結構的尺寸進行優(yōu)化設計,大幅減少了優(yōu)化設計所需時間。4、對GPS北斗雙模微帶天線的頻率特性進行高斯過程建模,解決了較窄的頻帶范圍內對特殊頻率點有要求的天線頻率特性建模問題,建立起精確度比較高的高斯過程模型,融合到粒子群算法中,對該天線結構尺寸進行了優(yōu)化設計,大幅減少了天線優(yōu)化設計所需時間。5、對雙脊喇叭天線的頻率特性進行高斯過程建模,解決了寬頻帶天線頻率特性建模問題,建立起高精度高斯過程模型,融合到粒子群算法中,對雙脊喇叭天線結構尺寸進行了優(yōu)化設計,大幅減少了該天線優(yōu)化設計所需時間。
[Abstract]:When the antenna is optimized, it can be realized by combining electromagnetic simulation software HFSS and particle swarm optimization algorithm, but it takes a lot of time to use HFSS to evaluate the fitness of particle swarm optimization algorithm. At the same time, there is a high demand for computer performance, which makes the antenna design of complex structure difficult. In this paper, Gao Si process modeling method is integrated into particle swarm optimization (PSO) algorithm for antenna with various uses and frequency bands, and the antenna is optimized to meet the requirements of the design index. At the same time, the method of combining particle swarm optimization algorithm with HFSS software is compared, and it is proved that the Gao Si process-particle swarm optimization algorithm has great advantages in the optimization time. The main research work in this paper is as follows: 1. The establishment and evaluation methods of Gao Si process model are introduced. Taking the rectangular side-fed microstrip antenna model as an example, it is shown that Gao Si process model has certain accuracy in predicting the frequency characteristics of the antenna. This paper introduces the idea of Gao Si process-Particle Swarm Optimization Joint algorithm, and compares this method with the method of using electromagnetic simulation software HFSS as the fitness evaluation method of Particle Swarm Optimization algorithm from the point of view of time. The advantages of the Gao Si process-particle swarm joint algorithm are illustrated. 3, the Gao Si process modeling for the frequency characteristics of printed dipole antennas is carried out, which solves the problem of antenna frequency characteristics modeling with bandwidth requirements. The Gao Si process model is incorporated into the particle swarm optimization algorithm to optimize the size of some structures of printed dipole antenna, which greatly reduces the time required for optimization design. 4, The frequency characteristics of GPS dual-mode microstrip antenna are modeled by Gao Si process, which solves the problem of antenna frequency characteristic modeling for special frequency points in a narrow band range, and sets up a Gao Si process model with high accuracy. The structure size of the antenna is optimized by the particle swarm optimization algorithm, which greatly reduces the time required for the optimization design of the antenna. 5. The frequency characteristics of the antenna with double ridged horn are modeled by Gao Si process. The problem of frequency characteristic modeling of broadband antenna is solved. A high precision Gao Si process model is established and incorporated into particle swarm optimization algorithm. The structural size of the antenna with double ridged horn is optimized, which greatly reduces the time required for the optimum design of the antenna.
【學位授予單位】：江蘇科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN820;TP18
本文編號：2443254
[Abstract]:When the antenna is optimized, it can be realized by combining electromagnetic simulation software HFSS and particle swarm optimization algorithm, but it takes a lot of time to use HFSS to evaluate the fitness of particle swarm optimization algorithm. At the same time, there is a high demand for computer performance, which makes the antenna design of complex structure difficult. In this paper, Gao Si process modeling method is integrated into particle swarm optimization (PSO) algorithm for antenna with various uses and frequency bands, and the antenna is optimized to meet the requirements of the design index. At the same time, the method of combining particle swarm optimization algorithm with HFSS software is compared, and it is proved that the Gao Si process-particle swarm optimization algorithm has great advantages in the optimization time. The main research work in this paper is as follows: 1. The establishment and evaluation methods of Gao Si process model are introduced. Taking the rectangular side-fed microstrip antenna model as an example, it is shown that Gao Si process model has certain accuracy in predicting the frequency characteristics of the antenna. This paper introduces the idea of Gao Si process-Particle Swarm Optimization Joint algorithm, and compares this method with the method of using electromagnetic simulation software HFSS as the fitness evaluation method of Particle Swarm Optimization algorithm from the point of view of time. The advantages of the Gao Si process-particle swarm joint algorithm are illustrated. 3, the Gao Si process modeling for the frequency characteristics of printed dipole antennas is carried out, which solves the problem of antenna frequency characteristics modeling with bandwidth requirements. The Gao Si process model is incorporated into the particle swarm optimization algorithm to optimize the size of some structures of printed dipole antenna, which greatly reduces the time required for optimization design. 4, The frequency characteristics of GPS dual-mode microstrip antenna are modeled by Gao Si process, which solves the problem of antenna frequency characteristic modeling for special frequency points in a narrow band range, and sets up a Gao Si process model with high accuracy. The structure size of the antenna is optimized by the particle swarm optimization algorithm, which greatly reduces the time required for the optimization design of the antenna. 5. The frequency characteristics of the antenna with double ridged horn are modeled by Gao Si process. The problem of frequency characteristic modeling of broadband antenna is solved. A high precision Gao Si process model is established and incorporated into particle swarm optimization algorithm. The structural size of the antenna with double ridged horn is optimized, which greatly reduces the time required for the optimum design of the antenna.
【學位授予單位】：江蘇科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN820;TP18
【參考文獻】
相關期刊論文 前10條
1 曹衛(wèi)平;楊昭;張惠敏;;基于改進粒子群算法的陣列天線方向圖綜合設計[J];桂林電子科技大學學報;2016年06期
2 康軍;段宗濤;唐蕾;劉研;王超;;高斯過程回歸短時交通流預測方法[J];交通運輸系統(tǒng)工程與信息;2015年04期
3 王鑫;李紅麗;;臺風最大風速預測的高斯過程回歸模型[J];計算機應用研究;2015年01期
4 李振剛;;基于高斯過程回歸的網(wǎng)絡流量預測模型[J];計算機應用;2014年05期
5 張樂;劉忠;張建強;任雄偉;;基于人工蜂群算法優(yōu)化的改進高斯過程模型[J];國防科技大學學報;2014年01期
6 張樂;劉忠;張建強;任雄偉;;一種改進高斯過程的回歸建模方法[J];華中科技大學學報(自然科學版);2013年10期
7 蘇國韶;武振興;燕柳斌;;工程結構優(yōu)化的進化策略-高斯過程協(xié)同優(yōu)化方法[J];計算力學學報;2013年05期
8 何志昆;劉光斌;趙曦晶;王明昊;;高斯過程回歸方法綜述[J];控制與決策;2013年08期
9 何志昆;劉光斌;趙曦晶;劉冬;張博;;基于GPR模型的自適應平方根容積卡爾曼濾波算法[J];航空學報;2013年09期
10 柯騰龍;趙小瑩;丁憶涵;李璨;;基于蟻群算法設計的多頻微帶天線[J];微波學報;2013年01期
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