臺灣博碩士論文加值系統

本論文提出一種改良型適應壓縮因子粒子群聚最佳化演算法(Modified adaptive fuzzy with a constriction factor particle swarm optimization, I-AFPSO )。利用演算法中群體最佳位置與個體最佳位置來獲得搜索能力參數，此參數與所設定之模糊規則庫輸入至模糊控制系統更新加速度係數，以此方式讓粒子於不同之情況下能動態調控其特性與趨勢，進一步改善演算法之精確度與效率性。本文完整建構I-AFPSO並以五種知名的多極值測試函數，於三種不同的維度下實驗以驗證演算法之效能。而後本文介紹適應型模糊滑動模式應用於Lorenz-Chen系統同步問題之控制，其控制方式為利用迫近模式讓主系統與從系統之誤差狀態趨至滑動平面，並於滑動模式下利用適應性方式預估模糊滑動補償器之輸出量。經數值模擬可看出此控制方式可以解決Lorenz-Chen系統同步問題，讓主-從系統之信號能於一穩定之誤差範圍內達到同步。最後本文將整合I-AFPSO於滑動模式系統中以做進一步優化，藉由訓練滑動控制器內可調控之正向常數並於演算法內計算誤差適應值，而訓練後所獲得之常數使模糊滑動控制器能讓信號獲得更小之誤差，即系統能得到更精確之同步。

In this paper, the modified particle swarm optimization method which combines modified adaptive fuzzy with a constriction factor (I-AFPSO) is investigated. The capacity parameters of I-AFPSO calculated by the best position of each particles and the best position of the group are inputted to fuzzy control system with fuzzy rule base for on-line updating the accelerating coefficients, which can dynamically adapt the characteristic of particle trend to improve the accuracy and efficiency of proposed algorithm. Furthermore, the adaptive fuzzy sliding mode control scheme for Lorenz-Chen systems synchronization is also investigated in this thesis. In this scheme, the approaching mode demanded to drive the error states of Lorenz-Chen systems to the sliding surface based on an adaptive fuzzy system to output controller in sliding mode. Some numerical simulations are provided to demonstrate the main results. Finally, I-AFPSO is applied in AFSMC to training the positive variable and counting the fitness for reducing the error signal value and more precise in the synchronizations characteristic.

摘 要...............................................................I
ABSTRACT...........................................................II
致 謝.............................................................III
目 錄..............................................................IV
圖 目 錄..........................................................VII
表 目 錄...........................................................XI
符 號 目 錄.......................................................XII
第一章 緒 論.......................................................1
1.1 前 言...........................................................1
1.2 研究動機與目的..................................................4
1.3 論文章節及架構..................................................5
第二章 粒子群聚最佳化演算法.........................................7
2.1 最佳化理論......................................................7
2.2 智能群.........................................................11
2-3 粒子群聚最佳化演算法...........................................14
2.3.1 發展背景.....................................................14
2.3.2 基礎理論與原理...............................................14
2.3.3 PSO執行程序與數學式表示......................................15
2.3.4 PSO運作流程與流程圖..........................................17
2-4 慣性權重式粒子群聚最佳化演算法.................................20
2-5 壓縮因子式粒子群聚最佳化演算法.................................22
第三章 適應型壓縮因子式粒子群優化演算法............................23
3.1 模糊控制簡介...................................................24
3.2 模糊適應型壓縮因子式粒子群最佳化...............................26
3.3 改良模糊適應型壓縮因子式粒子群最佳化...........................29
第四章 數值模擬與結果..............................................35
4.1 Spherical測試函數結果..........................................36
4.2 Ackley測試函數結果.............................................40
4.3 Griewank測試函數結果...........................................44
4.4 Rosenbrock 測試函數結果........................................48
4.5 Quadric測試函數結果............................................52
第五章 I-AFPSO優化適應性模糊滑動模式控制器於渾沌同步系統之應用.....56
5.1 滑動模式控制簡介...............................................56
5.2 標準型滑動模式控制設計.........................................59
5.3 積分型滑動模式控制器設計.......................................63
5.4 模糊適應型滑動模式控制器.......................................67
5.5 渾沌理論.......................................................71
5.6 Lorenz渾沌系統.................................................72
5.7 Chen 渾沌系統..................................................74
5.8 渾沌同步系統控制器設計.........................................76
5.9 I-AFPSO應用於改良渾沌同步滑動控制器............................83
第六章 結論與未來展望..............................................89
6.1 結論...........................................................89
6.2 未來展望.......................................................90
參考文獻...........................................................91

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