臺灣博碩士論文加值系統

本論文提出了改良型帝國主義競爭演算法應用於補償性類神經模糊系統。所提出的改良型帝國主義競爭演算法包含兩種方法分別為簡化型帝國主義競爭演算法和聯合帝國主義競爭演算法。本論文主要分為兩大部分。第一部份，我們提出簡化型帝國主義競爭演算法。在此演算法中，我們為了減少原始帝國主義競爭演算法的參數設置，採用高斯分佈的概念針對原始帝國主義競爭演算法的同化階段來改善殖民地的解，並且可以防止陷入局部最佳解以及提高全域最佳解的搜索能力。在第二部分中，我們提出聯合帝國主義競爭演算法。此演算法被提出主要是為了有效的平衡搜索空間局部以及全域探索的能力。此演算法是基於粒子群最佳化的速度和位置更新方程式來做發展和開發。因此，聯合帝國主義競爭演算法中設計的同化階段有三種主要考量。第一，殖民地從以前到現在的整個搜索空間中找出最佳的位置。第二，殖民地會朝所有帝國中最佳的帝國前進。第三，殖民地本身會朝向自己所屬的帝國前進。最後，本論文將所提出的兩種演算法應用於補償性類神經模糊系統並且應用在不同的預測問題上。本論文的實驗結果得以驗證了所提出的演算法之有效性。

This dissertation proposes improve imperialist competitive algorithms (IICA) for compensatory neural fuzzy systems (CNFS) model. This study proposes IICA includes the bare-bone imperialistic competitive algorithm (BBICA) and united-based imperialistic competitive algorithm (UICA). This dissertation consists of the two major parts. In the first part, the proposed BBICA is to reduce parameter setting of original ICA. BBICA adopts Gaussian distribution to improve assimilation phase of ICA and prevent BBICA from falling into local optimal solution and boot the explorative ability of the global. In the second part, the UICA method is presented to balance the local and global exploration of search space effecitively. The proposed UICA method which adopts updated formulas of particle swarm optimization velocity and position introduces to the assimilation of imperialist competitive algorithm. Therefore, the assimilation policy consists of three major parts. Firstly, the colony searches for the optimal position, starting with previous spaces and continuing to the entire current space. Secondly, the colonial empire faces forward toward the location at which it belongs. The colonial empire moves all of the strongest empires forward in the third part. Finally, the proposed two algorithms for CNFS model are applied in various prediction problems. Experiment results of this dissertation demonstrate the effectiveness of the proposed algorithms.

Abstract in English...i
Abstract in Chinese...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...4
Chapter 2 Related Works...6
2.1 Structure of Compensatory Neural Fuzzy Systems...6
2.2 The Particle Swarm Optimization...9
2.3 The Bare Bones Particle Swarm Optimization...11
2.4 The Imperialist Competitive Algorithm...12
Chapter 3 Bare-Bone Imperialist Competitive Algorithm for the CNFS Model...15
3.1 Bare-Bone Imperialistic Competitive Algorithm...15
3.1.1 Initialization Phase...17
3.1.2 Assimilation Phase...20
3.1.3 Competition Phase...23
Chapter 4 United-Based Imperialistic Competitive Algorithm for the CNFS Model...26
4.1 United-Based Imperialistic Competitive Algorithm...26
4.1.1 Initialization Phase...27
4.1.2 Assimilation Phase...28
4.1.3 Competition Phase...32
Chapter 5 Experimental Results...34
5.1 Example 1: Function approximate...34
5.1.1 The Comparison of Performance for Example 1...35
5.2 Example 2: Prediction of Mackey-Glass Chaotic Time Series...38
5.2.1 The comparison of performance for Example 2...39
5.3 Example 3: Prediction of Practical Time Series...42
5.3.1 The comparison of performance for Example 3...42
Chapter 6 Conclusions and Future Works...46
Reference...48
Extended Abstract...53
Curriculum Vitae...60

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