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研究生:莊啟元
研究生(外文):Chi-Yuan Juang
論文名稱:以改良適應型粒子群最佳演算法設計類神經網路
論文名稱(外文):Artificial Neural Networks Design Based On Modified Adaptive Particle Swarm Optimization
指導教授:蘇德仁蘇德仁引用關係
指導教授(外文):Te-Jen Su
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電子工程系
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:2009
畢業學年度:97
語文別:英文
論文頁數:54
中文關鍵詞:類神經網路倒傳遞法則粒子群最佳演算法改良適應型粒子群最佳演算法
外文關鍵詞:artificial neural networksback-propagationparticle swarm optimizationmodified adaptive particle swarm optimization
相關次數:
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  • 下載下載:5
  • 收藏至我的研究室書目清單書目收藏:0
本篇論文旨在使用改良適應型粒子群體最佳演算法,尋找類神經網路之最佳權重值。使用倒傳遞法則是利用一個近似最陡坡降法的演算方式,但因為初始權重的關係,使其往往只能求得局部最佳解,無法保證求得全域最佳解。粒子群最佳演算法是一種可以使所有個體朝同一方向與目標前進的方法,因此可避免陷入局部最佳解。
但是在參數選擇上多依靠經驗選取,參數的選取會直接影響到穩定性和最佳化性能。因此我們提出改良適應型粒子群最佳演算法。其中主要調整粒子群最佳演算法中的慣性權重,使之以指數遞減方式逐漸減少。
最後的研究將驗證我們提出的方法可以獲得最佳的結果。
In this thesis, the weights of the artificial neural networks (ANN) are trained by modified adaptive particle swarm optimization (MAPSO). Back-propagation (BP) is an approximate steepest descent algorithm, but BP often finds the local optimal solution not the global optimal solution, because of the initial weights. The particle swarm optimization (PSO) method is one of the most powerful methods for letting the entire individuals move to the target, hence it can avoid to get the local optimal solution.
However, the parameters, which greatly influence the algorithm stability and performance, are selected by depending on the designers’ experience. MAPSO is hereby presented with exponential decrease weights.
Finally, the demonstrated examples are presented to illustrate the better performance of the proposed methodology (MAPSO-ANN).
Abstract in Chinese..................................i
Abstract.............................................ii
Acknowledgements.....................................iii
Contents.............................................iv
List of Figures......................................v
List of Tables.......................................vi
Chapter 1. Overview..................................1
1.1 Background and Motivation....................1
1.2 Organization of the Thesis...................3
Chapter 2. Artificial Neural Networks................4
2.1 Biological Inspiration.......................5
2.2 Neuron Model and Networks Topologies.........7
2.3 Learning Rules...............................11
Chapter 3. Particle Swarm Optimization...............13
3.1 Swarm Intelligence...........................13
3.2 Evolutionary Computation.....................15
3.3 Methodology of Particle Swarm Optimization...17
3.4 Adaptive PSO Algorithm.......................25
3.5 Modified Adaptive PSO Algorithm..............26
Chapter 4. Neural Networks Design Based on MAPSO.....28
4.1 Back-Propagation Algorithm...................28
4.2 Back-Propagation Design Based On MAPSO.......36
Chapter 5. Simulation Results........................39
5.1 Introduction.................................39
5.2 Results......................................40
Chapter 6. Conclusions...............................49
References...........................................50
List of Publications.................................53
Biography............................................54
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