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研究生:莊賀鈞
研究生(外文):Ho-Chin Chuang
論文名稱:以有效的免疫系統為基礎之共生進化粒子群最佳化演算法設計類神經模糊網路
論文名稱(外文):Efficient Immune-Based Symbiotic Particle Swarm Optimization Learning for TSK-Type Neuro-Fuzzy Networks Design
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:朝陽科技大學
系所名稱:資訊工程系碩士班
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:2006
畢業學年度:94
語文別:英文
論文頁數:71
中文關鍵詞:臉部辨識預測共生進化粒子群最佳化演算法免疫系統演算法類神經模糊網路
外文關鍵詞:face detectionpredictionsymbiotic evolutionparticle swarm optimizationimmune algorithmTSK-type neuro-fuzzy networks
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本論文主要提出兩個新的學習演算法設計類神經模糊網路。近年來,許多研究議題應用免疫系統演算法於電腦科學與工程領域中,但所應用的免疫系統並無重大突破與改進,為了提昇免疫系統的效能,我們利用類神經模糊網路,並提出有效的以免疫系統為基礎之粒子群最佳化與以免疫系統為基礎之共生進化粒子群最佳化演算法,成功地解決了辨識、預測及臉部辨識的問題。在有效的免疫系統為基礎之粒子群最佳化中,為了避免落入最佳區域解及並接近全域最佳解之搜尋能力,突變是一個很重要的機制,因此,我們利用了粒子群最佳化的優勢當作突變機制,其優點為易於找出全域最佳解、易懂與實作簡單。然而,粒子群最佳化收斂速度能力之關鍵是取決於參數(ω, r1及r2)設定。傳統粒子群最佳化並不能保證經過演化後能得到最佳解,原因在於參數的設定為隨機選取,為了改善此缺點,我們加入混沌對應的能力來改善粒子群最佳化全域搜尋的能力,並修正粒子的方程式,使在調整參數時對系統整體效能的影響更優於傳統的粒子群演算法。此外,對於免疫系統的架構,做了重大突破的提升,傳統的免疫系統將一個個體視為一個解,我們加入了共生進化的策略,僅將一個個體視為某部份的區域解,最後的解由許多代表區域解的個體所組成,更能有效地解決複雜的問題與提升系統的效能。
In this thesis, we propose two new learning algorithms to design the TSK-type neuro-fuzzy networks. Though, there has been a great deal of interest in the use of the immune systems and algorithms as inspiration for computer science and engineering, in the fundamental methodologies it is not dramatic. In order to enhance the IA performance, we propose the efficient immune-based particle swarm optimization (IPSO) and the immune-based symbiotic particle swarm optimization (ISPSO) with TSK-type neuro-fuzzy networks for solving the identification, prediction and face detection problems. The proposed IPSO is combining the IA and PSO to perform parameter learning. In order to avoid trapping in a local optimal solution and to ensure the searching capability of near global optimal solution, mutation plays an important role in IPSO. Therefore, we employed the merits of PSO to improve mutation mechanism. The PSO algorithm has proved to be very effective for solving global optimization. It is not only a recently invented high-performance optimizer that is very easy to understand and implement, but also requires less computational bookkeeping and generally only a few lines of code. However, the parameters (ω, r1 and r2) of PSO are the key factors to affect the convergence. In fact, parameters of PSO cannot ensure the optimization’s ergodicity entirely in phase space because they are absolutely random in the traditional PSO. Moreover, we propose another measure which introducing chaotic mapping with certainty, ergodicity and the stochastic property into PSO so as to improve the global convergence. In addition to modify the PSO, we improve the IA structure. Unlike the IA that uses each individual (antibodies) in a population as a full solution to a problem, symbiotic evolution assumes that each individual in a population represents only a partial solution to a problem; complex solutions combine several individuals in the population.
Contents
Abstract in Chinese I
Abstract in English III
Contents VI
List of Tables VIII
List of Figures IX
Chapter 1: INTRODUCTION 1
1.1 Motivation 1
1.2 Literature Review 4
1.3 Thesis Organization 7
Chapter 2: THE STRUCTURE OF A TSK-TYPE NEURO-FUZZY NETWORK 8
Chapter 3: THE EFFICIENT IMMUNE-BASED PARTICLE SWARM OPTIMIZATION (IPSO) 12
3.1 The Basic Concepts of the Immune System 12
3.2 Implementation of Efficient Immune-Based Particle Swarm Optimization 14
3.3 Illustrative Examples 24
Chapter 4: THE IMMUNE-BASED SYMBIOTIC PARTICLE SWARM OPTIMIZATION (ISPSO) 39
4.1 The learning algorithm of the immune-based symbiotic particle swarm optimization 40
4.2 Illustrative Examples 49
Chapter 5: CONCLUSION 62
Bibliography 64
Vita 70
Publish List 71
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