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研究生:王鈺潔
研究生(外文):Yu-Jie Wang
論文名稱:自適應解分享粒子群演算法及其在螺旋電感最佳化設計之應用
論文名稱(外文):Adaptive Solution-Sharing Particle Swarm Optimization and Its Application of the Design of the Spiral Inductor
指導教授:莊堯棠
指導教授(外文):Y.-T. Juang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:97
中文關鍵詞:粒子群演算法螺旋電感
外文關鍵詞:Particle Swarm Optimizationspiral inductor
相關次數:
  • 被引用被引用:5
  • 點閱點閱:117
  • 評分評分:
  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
本論文中,我們提出了一種改良式粒子群演算法,名為自適應解分享粒子群演算法(Adaptive Solution-Sharing Particle Swarm Optimization, ASSPSO),並應用於射頻積體電路(Radio Frequency Integrated Circuits, RFIC)之螺旋(Spiral)電感最佳化設計。在標準粒子群演算法中每一個體使用共同的慣性權重,且如果適應函數維度較大時,前期收斂速度會十分緩慢,為了解決這個問題,本論文使用唯一認知模型(Cognition Only Model),以提升前期的收斂速度,並且針對慣性權重提出改良方法,利用粒子與粒子之間的差異度去更新移動方程式,可以更有效的從局部搜索開始,使粒子具有比較好的搜尋能力往全域最佳解移動,並且和幾個已經提出的改良式粒子群演算法做性能的比較,同時也顯示本文所提出的改良式粒子群演算法能有效地改善標準粒子群演算法容易陷入局部最佳解的缺點,最後應用於螺旋電感最佳化設計。在無線通訊系統中,射頻積體電路的特性好壞與電感的品質因數有密切的關係,以低雜訊放大器舉例,現階段以CMOS製程出來的螺旋(Spiral)電感之品質因素較低,使低雜訊放大器指數(NF)增加、增益(Av)下降造成電路效能不佳,所以本論文利用提出的改良式粒子群演算法有效提升電感之品質因數,以提高整個射頻積體電路(RFIC)的效能。
In this thesis, we propose a variant algorithm for Particle Swarm Optimization (PSO) which is called Adapted Solution-Sharing Particle Swarm Optimization (ASSPSO), and applied to optimization of spiral inductor of Radio Frequency Integrated Circuits (RFIC). In standard PSO algorithm, each particle of SPSO using a same equation to update a particle’s velocity. If the dimension is large, the convergence rate will be very slow and get local easily. In order to solve this problem, the proposed ASSPSO uses a cognition only model to enhance the particle’s velocity and according to the distance between particle and the best particle with best fitness to adjust the inertia weight adaptively. For the particle with a better fitness, the inertia weight is decreased; otherwise the inertia weight is increased for particles with inferior fitness. The methods cause the fast convergence ability in pre-convergence and have less time on computing. The performance of ASSPSO is fairly demonstrated by applying sixteen benchmark problems and compared it with several popular PSO algorithms. Finally, the modified PSO algorithm will be applied to optimization of spiral inductor. In wireless communication systems, spiral inductors are the essential component of radio frequency integrated circuits (RFIC). The performance of radio frequency integrated circuit is decided by the quality factor (Q). Taking Low-Noise Amplifier (LNA) for example, the quality factor of LNA is lower in CMOS process. It will lead to a bad performance of circuits. So we will take advantage of modified PSO algorithm on optimization of spiral inductor to make it better.
摘要 I
Abstract II
目錄 IV
圖目錄 VI
表目錄 IX
第一章 緒論 p1
1.1研究動機 p1
1.2論文架構 p3
第二章 粒子群演算法 p4
2.1人工智能演化最佳化方法 p4
2.2傳統粒子群演算法介紹 p5
2.3粒子群演算法基本公式與模式 p5
2.4慣性權重 p6
第三章 改良式粒子群最佳化方法暨模擬 p10
3.1自適應慣性權重 p10
3.1.1自適應慣性權重原型 p11
3.1.2修正參數α p12
3.2粒子群解分享機制 p14
3.2.1模範粒子 p14
3.2.2Refreshing Gap m p16
3.3自適應解分享粒子群演算法 p30
3.4模擬實驗結果 p33
3.4.1測試函數10維之結果 p38
3.4.2測試函數30維之結果 p49
第四章 電感之元件特性介紹 p60
4.1電感元件上之損耗與寄生效應 p60
4.2電感自振 p60
4.3金屬損耗 p61
4.3.1導體損耗 p61
4.3.2渦狀電流損耗 p62
4.3.3集膚效應(Skin Effect) p62
4.4基板損耗 p63
4.4.1電場穿透 p63
4.4.2磁場損耗 p64
4.5電感之等效模型 p65
第五章 改良式粒子群演算法於螺旋電感最佳化Q值實驗結果與分析 p68
5.1電感模型模擬 p68
5.2螺旋電感最佳化之模擬結果 p72
第六章 總結與未來展望 p77
6.1總結 p77
6.2未來展望 p78
參考文獻 p79

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