臺灣博碩士論文加值系統

模糊邏輯理論最早是由Zadeh所提出。其所提出的模糊集合主要觀念是將人類的經驗法則轉化為模糊推論所使用的模糊規則，並以數學函數將其歸屬度量化來進行推論。
在本篇論文中，我們提出一個適合類梯形歸屬函數之高速模糊推論處理器。我們所提出的模糊推論處理器，最大的特色是在模糊推論前，先行分析輸入變數與模糊規則庫的關係。因此能夠及早找出與輸入變數有所交集的歸屬函數，並且去除與模糊推論結果無貢獻的規則。由於只有與輸入變數有交集的歸屬函數所組成的模糊規則才會拿來作模糊推論，因此可以大幅節省推論時間。
我們使用CIC所提供的0.35μm標準元件庫，來實現此模糊推論處理器。經由時序分析結果可知其工作頻率達190MHz，推論速度最快可達23.7MFLIPS (Mega Fuzzy Logic Inferences Per Second)。與其他架構比較，我們所提出的架構之適用性及推論速度都有很好的表現。

The theorem of fuzzy logic was presented from Zadeh at the earliest. The main concept of the fuzzy set proposed by Zadeh is to convert the experience rule of human being into the fuzzy set of the fuzzy inference and quantify its membership by mathematical.
We proposed a high-speed fuzzy inference processor suitable for the trapezoid-shaped membership function in this paper. The most outstanding characteristic of the proposed fuzzy inference processor is that we analyze the relationship between input variables and fuzzy rules first. So that we can find the membership functions that overlaps the input variables as soon as possible. And ignore the fuzzy rules those are not contributed to the result of the fuzzy inference. We can slash inference time by a wide margin because there are only fuzzy rules composed of the membership functions that overlap the input variable can we use to proceeding fuzzy inference.
We use 0.35μm standard cell library presented by CIC to implement the fuzzy inference processor. We can know its working frequency reaches to 190MHz and its inference velocity is up to 23.7MFLIPS (Mega Fuzzy Logic Inferences Per Second) through the result of timing analysis. Compared with other architecture, the suitability and inference velocity of the architecture proposed in this paper exhibit quite well.

目錄

誌謝 I
摘要 II
ABSTACT III
目錄 V
圖索引 VIII
表索引 XI
第一章 序論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 全文架構 4
第二章 模糊邏輯理論與模糊推論處理器 5
2.1 模糊邏輯 5
2.2 模糊推論運算 7
2.3 模糊推論處理器 10
2.4 文獻回顧 12
第三章 具備規則分析技術之高速模糊推論處理器設計 19
3.1 設計動機 19
3.2 模糊規則庫 23
3.3 管線式系統架構 25
3.3.1 判斷單元 26
3.3.2 排序單元 29
3.3.3 讀取模糊規則單元 34
3.3.4 模糊解碼單元 36
3.3.5 模糊判斷單元 45
3.3.6 最大值單元 49
3.3.7 解模糊化單元 52
第四章 實驗結果 58
4.1 模糊推論處理器效能分析及比較 58
4.2 模糊推論處理器應用模擬 64
4.2.1 倒車入庫系統 64
4-2-2 車載平衡桿系統 67
第五章 結論 70
參考文獻 71
附錄A 口試委員問答 76


圖索引

圖1-1 模糊推論處理器 2
圖1-2 類梯形歸屬函數表示法 3
圖2-1 一個歸屬函數範例 6
圖2-2 模糊推論運算 8
圖2-3 模糊推論運算範例 10
圖2-4 文獻[17]所推薦之類梯形歸屬函數表示法 12
圖2-5 歸屬函數與輸入變數的關係圖：(A)明確值輸入；(B)模糊輸入 14
圖3-1 模糊輸入變數與歸屬函數交集範例 22
圖3-2 歸屬函數 23
圖3-3 提出的模糊推論處理器架構 25
圖3-4 二元搜尋法 26
圖3-5 利用二元搜尋法來搜尋與輸入變數X有交集的起始歸屬函數位址 27
圖3-6 排序單元 30
圖3-7 最大－最小值運算所有可能狀況分析 38
圖3-8 交點計算 40
圖3-9 最大－最小值計算單元 41
圖3-10 模糊解碼單元 42
圖3-11 模糊判斷邏輯 46
圖3-12 模糊判斷單元 48
圖3-13 模糊判斷運算範例 49
圖3-14 最大值單元 50
圖3-15 解模糊化單元 54
圖3-16 解模糊化範例 57
圖4-1 晶片佈局圖 59
圖4-2 文獻[7]所使用的歸屬函數 60
圖4-3 文獻[26]所使用的歸屬函數 61
圖4-4 文獻[27]所使用的歸屬函數 61
圖4-5 文獻[28]所使用的歸屬函數 62
圖4-6 文獻[20]所提出之演算法調整後的非線性控制系統歸屬函數 62
圖4-7 倒車入庫系統示意圖 64
圖4-8 倒車入庫系統使用之歸屬函數 65
圖4-9 MATLAB模擬倒車入庫系統之控制平面 66
圖4-10 VERILOG-XL模擬倒車入庫系統之控制平面 66
圖4-11 車載平衡桿系統示意圖 67
圖4-12 車載平衡桿系統使用之歸屬函數 68
圖4-13 MATLAB模擬車載平衡桿系統之控制平面 69
圖4-14 VERILOG-XL模擬車載平衡桿系統之控制平面 69
圖A-1 倒車入庫系統路徑示意圖 79



表索引

表3-1 模糊規則庫前項部分範例 24
表3-2 與輸入變數X對應的歸屬函數範例 28
表3-3 排序單元動作機制範例 33
表3-4 模糊規則庫範例 36
表3-5 最大值單元運算運算範例 51
表4-1 有貢獻之模糊規則數與模糊推論處理器之推論速度關係表 59
表4-2 模糊推論處理器比較表 63
表4-3 倒車入庫使用之模糊規則庫 65
表4-4 車載平衡桿系統使用之模糊規則庫 68

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