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研究生:陳玠含
研究生(外文):Chieh-Han Chen
論文名稱:以策略型細菌覓食演算法之模糊小腦模型及其於分類之應用
論文名稱(外文):A Novel Strategy-adaptation-based Bacterial Foraging Optimization for FCMAC Model and Its Applications in Classification
指導教授:林正堅林正堅引用關係
指導教授(外文):Cheng-Jian Lin
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:資訊工程系
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:2013
畢業學年度:101
語文別:英文
論文頁數:86
中文關鍵詞:細菌覓食演算法策略方法小腦模型模糊理論分類
外文關鍵詞:Bacterial foraging optimizationstrategy adaptationcerebellar model articulation controllerfuzzy theoryclassification
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本論文提出一個具改良型的細菌覓食演算法之模糊小腦模型,此改良型的細菌覓食演算法稱之為策略型細菌覓食演算法,我們將利用策略型細菌覓食演算法來調整模糊小腦模型中的各個權重值,使得網路的實際輸出更能逼近期望輸出,以利於分類上之應用。本論文主要分為兩大部分,第一部分我們將提出一種新的策略型細菌覓食演算法。此演算法主要的貢獻為在細菌覓食演算法中融入了策略方法,讓每一隻細菌依照現況都擁有不同的移動步伐,增加細菌的多樣性,並以一些數值函數來驗證所提出的策略型細菌覓食演算法較其他方法容易達到最佳解。由於倒傳遞學習演算法常用於調整模糊小腦模型中的參數,但是它容易陷入區域解,為了希望得到最佳化的參數,近幾年中演化計算的方法常被使用,因此在第二部分中,我們將以所提出的策略型細菌覓食演算法用來調整模糊小腦模型中的參數。最後,從實驗結果中我們將與其他方法進行比較,證明我們所提出的演算法具有較佳的效能。
In this thesis, we propose a fuzzy cerebellar model articulation controller (FCMAC) model with improved bacterial foraging optimization (BFO). The proposed modified bacterial foraging optimization is called Strategy-adaptation-based Bacterial Foraging Optimization (SABFO). The SABFO is use to adjust the weights of FCMAC model and facilitate the actual output of the FCMAC model approximating the desired output for classifications. This thesis consisted of two major parts. In the first part, we propose a new Strategy-adaptation-based Bacterial Foraging Optimization. The main contribution of this study is adding the strategic approach into traditional bacterial foraging optimization. The propose method makes each bacterium perform different run-lengths, and increased bacterial diversity as well. We use nonlinear benchmark functions for verifying our propose SABFO to achieve the global optimal solution more easily than other methods. In the second part, we apply the propose SABFO to adjust the parameters of FCMAC model. Although back-propagation (BP) algorithm is commonly used to adjust the parameters of FCMAC model, it is easy to fall into the local optimal solution. Therefore, our proposed evolutionary algorithms can solve the above-mentioned optimal parameter problems successfully. Finally, the propose FCMAC model with SABFO learning algorithm is applied in classification problems. Experimental results demonstrate the convergence effectiveness of the proposed methods.
摘要 IV
Abstract VI
誌謝 VIII
Contents IX
List of Figures XI
List of Tables XII
Chapter 1. Introduction 1
1.1. Motivation 1
1.2. Literature Review 4
1.3. Thesis Organization 9
Chapter 2. A Novel Strategy-adaptation-based Bacterial Foraging Optimization 10
2.1. Review Traditional Bacterial Foraging Optimization 10
2.1.1. Real E. Coli Bacteria Behavior 10
2.1.2. Traditional Bacterial Foraging Optimization 11
2.2. The Proposed SABFO 15
2.3. Simulation Results 25
2.3.1. Test Functions 27
2.3.2. Comparisons with Other Methods 29
2.3.3. Discussion 33
Chapter 3. The Fuzzy Cerebellar Model Articulation Controller with SABFO 35
3.1. Review traditional CMAC model and FCMAC model 35
3.1.1. Review Traditional CMAC Model 35
3.1.2. Review FCMAC Model 38
3.2. The FCMAC Model with SABFO Leaning Algorithm 41
3.3. Simulation Results 43
3.3.1 Experimental Results on Iris Data 45
3.3.2 Experimental Results on Thyroid Data 49
3.3.3 Experimental Results on Breast Cancer Data 52
3.3.4 Experimental Results on Wine Data 56
3.3.5 Discussion 61
Chapter 4. Conclusions and Future Works 63
4.1. Conclusions 63
4.2. Future Works 64
Bibliography 65
Vitae 74

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