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研究生:張榮座
研究生(外文):Rong-Zuo Jhang
論文名稱:使用改良型人工蜂群演算法於類神經模糊網路
論文名稱(外文):Using Improved Artificial Bee Colony Algorithms for Neural Fuzzy Networks
指導教授:陳政宏陳政宏引用關係
指導教授(外文):Cheng-Hung Chen
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:電機工程研究所
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:73
中文關鍵詞:神經模糊網路人工蜂群演算法差分進化演算法以生物地理學為基礎的優化以獎懲為基礎輪盤式選擇混沌映射逼近函數混沌行為水浴溫度控制系統
外文關鍵詞:Neural fuzzy networksartificial bee colony algorithmdifferential evolutionbiogeography-based optimizationreward-based roulette wheel selectionchaotic mappiecewise functionchaotic behaviorswater bath temperature system.
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本論文提出了兩個演算法應用於類神經模糊網絡。這兩個演算法包括是改良型人工蜂群演算法和改良型人工蜂群演算法與混沌映射。本論文分為兩個主要部分。在第一部分中,我們提出一個改良型人工蜂群演算法。在此演算法中,我們採用差分進化演算法的突變策略當作原始的人工蜂群演算法的搜索公式來生成新的解並使用貪婪式選擇來決定引領蜂和跟隨蜂蜜蜂的解。此外,所提出的演算法還採用了一個以獎勵為基礎的輪盤式選擇,此輪盤式選擇將所有解分為可行的解和不可行的解,並利用獎勵和懲罰去改變各個解被選中的概率值。在第二部分中,我們提出一個改良型人工蜂群演算法與混沌映射有效的平衡改良型人工蜂群演算法探索和開發的能力。此演算法與第一部分演算法的不同在於引領蜂結合了差分進化的操作和以生物地理學為基礎優化的遷移操作得以產生新的解。另外為了避免改良型人工蜂群演算法陷入局部最優解,此演算法還利用了混沌映射來解決上述的問題。 最後,我們將所提出的兩種演算法應用於類神經模糊網路並實施在各種非線性控制系統的問題上。本篇論文的結果得以證實了所提出的演算法之有效性。

This dissertation proposes two algorithms for neural fuzzy networks (NFN) in nonlinear control problem. The two algorithms are including the improved artificial bee colony (IABC) algorithm, and improved artificial bee colony with chaotic map (IABC_CM) algorithm. This dissertation consists of the two major parts. In the first part, the IABC method is proposed for the NFN model. The IABC adopts operator of differential evolution (DE) as the search strategy of artificial bee colony (ABC) to generate new solution and uses greedy selection to decide better solution for employed and onlooker bees. Furthermore, the IABC also uses a reward-based roulette wheel selection will be initially to divide all solutions suitably into feasible and infeasible solutions; thereafter, it divides them based on feasible and infeasible solutions for the implementation of incentives and punishments. In the second part, the IABC_CM is presented to balance the exploration and exploitation of the IABC algorithm effectively. The IABC_CM combines DE operator and migrate operator of BBO to produce new solution for employed bees. In order to avoid IABC method trapped into local optimum, the IABC_CM is utilized chaotic map to solve the above problem. Finally, the proposed two algorithms are applied to implement NFN model in various nonlinear control system problems. The results of this dissertation demonstrate the effectiveness of the proposed algorithms.

English Abstract i
Chinese Abstract iii
Acknowledgment v
Contents vi
List of Tables viii
List of Figures ix
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Organization of Dissertation 3
Chapter 2 Neural Fuzzy Networks 5
Chapter 3 Improved Artificial Bee Colony for the NFN Model 8
3.1 Principles of the Original Artificial Bee Colony Algorithm 8
3.1.1 Behavior of real bees 8
3.1.2 Artificial Bee Colony (ABC) Algorithm 11
3.2 Principles of the Differential Evolution Algorithm 13
3.3 The Improved Artificial Bee Colony 14
3.3.1 Initialization Food Source Positions 16
3.3.2 Calculate Nectar Amounts 16
3.3.3 Determine the New Food Source Positions for Employed Bees 17
3.3.4 Calculate Probability Value 18
3.3.5 Determine the New Food Source Positions for Onlooker Bees 19
3.3.6 Produce New Positions for the Exhausted Food Sources 19
3.3.7 Memorize the Position of the Best Food Source 19
Chapter 4 Improved Artificial Bee Colony with Chaotic Map for the NFN Model 20
4.1 Principles of the Biogeography-Based Optimization 20
4.2 Chaos Theory 23
4.3 The Improved Artificial Bee Colony with Chaotic Map 24
4.3.1 Use Biogeography-Based to Determine the New Food Source Positions for Employed Bees 26
4.3.2 Use Chaotic Map to Produce New Positions for the Exhausted Food Sources 27
Chapter 5 Simulation Results 30
5.1 Example 1: Approximation of the Piecewise Function 30
5.1.1 Example Results of NFN-IABC for Example 1 31
5.1.2 Example Results of NFN-IABC_CM for Example 1 31
5.1.3 The Comparison of Performance for Example 1 32
5.2 Example 2: Learning Chaotic Behaviors 37
5.2.1 Example Results of NFN-IABC for Example 2 38
5.2.2 Example Results of NFN-IABC_CM for Example 2 40
5.2.3 The Comparison of Performance for Example 2 42
5.3 Example 3: Water Bath Temperature Control 44
5.3.1 Example Results of NFN-IABC for Example 3 47
5.3.2 Example Results of NFN-IABC_CM for Example 3 50
5.3.3 The Comparison of Performance for Example 3 53
Chapter 6 Conclusion and Future Works 58
Reference 60
Extended Abstract 65
Curriculum Vitae 72
Publication List 73


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