臺灣博碩士論文加值系統

在本文中，提出一種簡單式的基因演算法(Simplified Genetic Algorithm)用來調整模糊類經網路中的權重值及BMF(Bspline Membership Function)控制點。傳統模糊類神經網路透過梯度下降法學習，在學習過程中可能會產生落入區域極值的現象。在不同領域，基因演算法搜尋最佳值的特性已經受到廣泛的注意。因此許多研究者利用基因演算法來克服傳統梯度下降法所產生的問題。但是，傳統的基因演算法對於處理大量變數 (超過100) 編碼解碼的過程中會有兩個重大的缺點：第一個是過程中需要大量的計算，第二個是經過此過程照成精確度的偏差。在本文中，提出的簡易型基因演算法藉由循序搜尋交配點來保證子代的適應力會優方於母代。染色體由實數的方式組成，包括了模糊類經網路中的權重值及BMF控制點。SGA可以快速收斂到模糊類神經網路的最佳值已經在本文中証實。近年來，線上即時控制是一個重要的研究方向，但是以基因演算法做為基礎的線上即時控制，因為速算量過大會有控制力延遲的問題而無法做更進一步的研究。本文藉由SGA快速收斂的特性設計線上即時間接型適應控制器來控制機械手臂及倒單擺系統在模擬中得到不錯的效果。

In this thesis, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a simplified genetic algorithm (SGA) is proposed. Fuzzy-neural networks (FNN) are traditionally trained by using gradient-based methods, and may fall into a local minimum during the learning process. Genetic algorithms have drawn significant attentions in various fields due to their capabilities of directed random search for global optimization. This motivates the use of genetic algorithms to overcome the problem encountered by the conventional learning methods. However, it is well known that searching speed of the conventional genetic algorithms is not desirable. Thus far, such conventional genetic algorithms are inherently disadvantaged in dealing with a vast amount (over 100) of adjustable parameters in the fuzzy-neural networks. In this thesis, the SGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed as a single point crossover operation. Chromosomes consisting of both the control points of BMF’s and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the SGA. Adaptive control is a technique of applying some system identification techniques to obtain a model of a system from input-output data. GA on-line training is one of most significant studies of the subject. However, with traditional GA no further training of the FNN is possible, while the drive is operating since the training process is too slow for on-line training. Because of the use of the SGA, faster convergence of the evolution process to search for an optimal fuzzy-neural network can be achieved. Unknown systems identified by using the fuzzy-neural networks via the SGA are applied to adaptive fuzzy-neural control at the end of this thesis to illustrate the effectiveness and applicability of the proposed method.

CONTCENTS
ABSTRACT (In Chinese)…………………………………………………….i
ABSTRACT (In English)…………………………………………………..ii
ACKNOWLEDGEMENTS………………………………………………………….iii
CONTENTS…………………………………………………………………….iv
LIST OF FIGURES AND TABLES……………………………….…………….vi
CHAPTER 1 Introduction………………………………………………….1
CHAPTER 2 Fuzzy Control System…………………………………………………….5
2.1 Fuzzy Set and Set-Theoretical Operators……………………………..6
2.2 Fuzzifier……………………………………………………………….7
2.3 Defuzzifier…………………………………………………………….9
2.4 Fuzzy Rule Base……………………………………………………..10
2.5 Fuzzy Inference………………………………………………………11
CHAPTER 3 B-spline Fuzzy Neural Network (B-spline FNN)………………………..13
3.1 Knot vector…………………………………………………………..13
3.2 B-spline Curves……………………………………………………...14
3.3 B-spline Membership Function (BMF)……………………………...16
3.4 The Configuration of A B-spline FNN………………………………17
3.5 A B-spline FNN Inference Method………………………………….17
CHAPTER 4 Design of the FNN Identifiers by the Simplified Genetic Algorithms…..19
4.1 The Simplified Genetic Algorithm…………………………………..20
4.2 Basic Concept of Gas………………………………………………..20
4.3 Evolutionary Processes of the Simplified Genetic Algorithm (SGA).22
4.3.1 Population Initialization……………………………………….22
4.3.2 Fitness function………………………………………………...23
4.3.3 Single Point Crossover Operation……………………………..23
4.3.4 Sorting Operation……………………………………………...25
4.3.5 Mutation Operation……………………………………………25
4.4 Pseudo Code for The SGA…………………………………………..28
4.5 Computer Simulations……………………………………………….30
CHAPTER 5 INDIRECT ADAPTIVE FUZZY NEURAL NETWORK…………...…44
5.1 Control Objectives…………………………………………………...44
5.2 Certainty Equivalent Controller……………………………………...45
5.3 Supervisory Control…………………………………………….……46
5.4 Definition the Fitness Function of SGA-Based Indirect Adaptive Fuzzy-Neural Controller (SIAFC)………………………..……….48
CHAPTER 6 Simulations Results……………………………………………………...51
SHAPTER 7 Conclusions………………………………………………………………60
REFERENCES……………………………………………………………………...….61

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