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系統識別號 U0002-2607201213464100
中文論文名稱 模糊鑑別器於Burn In系統之設計
英文論文名稱 Fuzzy Identification for Burn-In System
校院名稱 淡江大學
系所名稱(中) 電機工程學系博士班
系所名稱(英) Department of Electrical Engineering
學年度 100
學期 2
出版年 101
研究生中文姓名 陳光原
研究生英文姓名 Kuang-Yuan Chen
學號 892350017
學位類別 博士
語文別 英文
口試日期 2012-06-01
論文頁數 98頁
口試委員 指導教授-江正雄
委員-黃聰亮
委員-呂學坤
委員-余繁
委員-蕭瑛東
委員-李世安
委員-翁慶昌
委員-江正雄
中文關鍵字 模糊系統  群聚分析演算法:奔應系統 
英文關鍵字 Fuzzy Systems  Clustering Algorithms  SOFM  Burn-In System 
學科別分類
中文摘要 建立一個模糊系統主要是依靠專家經驗來提供設計方法。如果一個受控系統只知道它的輸入輸出資料時,如何在沒有專家經驗下來建立適當的模糊鑑別器是很大的挑戰。本論文會探討設計一個模糊鑑別器的方法,藉著學習法則來不斷地學習輸入輸出的資料的行為,使其儘可能趨近於欲鑑別的系統。
在論文的第一部份,我們發展了一種適用於模糊空間切割的分群演算法,它可以有效的探勘所處理資料之群聚分佈狀態,分析欲鑑別系統輸入輸出資料的群聚關係,而所得到之結果則可用作模糊系統粗略式的結構鑑別。得到初步的模糊系統之後,便可以系統之輸入輸出資料做為訓練目標,進一步學習以細調模糊系統的參數,使之能夠更精確符合受鑑系統的行為。
在論文的第二部份,我們介紹一種基於競爭式學習的模糊系統建模方法。我們利用SOFM 神經網路在低維度矩陣空間內輸出拓樸網路來得到高維度輸入資料的推理法則,並產生有意義的規則資料庫以重塑受鑑別系統。而為了更精確逼近受鑑系統的特性,本論文將萃取出的模糊規則進一步結合遞迴式最小平方法進行參數鑑別的設計程序,來達到微調的效果。
論文的最後一個部份則是將模糊鑑別器的設計方法應用在一Burn-In測試系統的恆溫調節。在此Burn-In測試系統中,我們需要控制加熱器與散熱風扇讓測試溫度穩定在所設定範圍,才能達到對每一個待測物Burn-In的效果;傳統作法中使用PI控制器所實現的溫度控制系統需要耗費很多時間來調整控制參數,在上升時間與超越量等性能也不易滿足測試的需求。本論文使用模糊鑑別器的設計方法建立模糊控制器以操控風扇及加熱器之運作,實際結果發現除了減少參數調整的試誤時間外,對系統也有較快上升時間及較小的超越量。
英文摘要 To establish a fuzzy system is to rely on the experience of an expert to provide the design methods. For a controlled system, if we only know its input-output data, it would be a challenge to establish an appropriate fuzzy system without expert experience.
The first task of the dissertation is to develop the fuzzy space clustering algorithm. It can effectively explore the cluster distribution of the processed data and analyze the clustering of the input-output data of the identified system. We can use the input-output data of the identified system as a training target to tune the parameters of the fuzzy systems more precisely in line with the behavior of the identified system.
The second task of the dissertation is to introduce a competitive learning fuzzy system modeling approach.We use the topological network sent out from SOFM to get the meaningful fuzzy rules. We can use the input-output data as the training samples to further fine-tune the parameter of fuzzy inference system so that it can more accurately match the behavior of the system to be identified.
The last task of the dissertation is to apply the fuzzy indenitification design to approach a thermostatically controlled Burn-In test system. In this Burn-In test system, we need to control the heater and fan to keep the temperature stabilized in the set range. We use the propsed fuzzy identification method to build a fuzzy controller to manipulate the operaion of the fan and heater. The actual results show the method can reduce the time of trial-and-error for the parameter adjustment. The system also has faster rising time and smaller overshoot.
論文目次 Contents
Chapter 1
Introduction............................................1
1.1 Background..........................................1
1.2 Research objective .................................3
1.3 Paper architecture .................................3
Chapter 2
Problem Definition .....................................5
2.1 Introduction........................................5
2.2 Clustering problem .................................5
2.2.1 Definitions ......................................5
2.2.2 Distance and similarity...........................6
2.2.3 Clustering methods................................9
2.2.3.1 Hierarchical clustering........................10
2.2.3.2 Partitional clustering.........................12
2.2.3.3 K-means clustering ............................13
2.2.3.4 Fuzzy c-means clustering ......................14
2.2.3.5 Normalized fuzzy c-means clustering ...........15
2.2.4 Cluster validity ................................21
2.3 Artificial neural networks ........................24
2.3.1 Modeling a neuron................................24
Contents
II
2.3.2 Neural models in common use .....................30
2.3.2.1 Multilayer perceptrons.........................30
2.3.2.2 Radial basis function networks.................33
Chapter 3
A Clustering-based Algorithm to Extracting Fuzzy
Rules for System Modeling .............................36
3.1 Introduction.......................................36
3.2 Coarse model construction for fuzzy system using clustering
method.................................................37
3.3 Parameter identification for fuzzy system..........42
3.4 Experimental results ..............................43
3.5 Conclusion ........................................47
Chapter 4
An Approach for Fuzzy Modeling Based on
Self-Organizing Feature Maps Neural Network............48
4.1 Introduction.......................................48
4.2 Using the Topological Network of SOFM to Realize the Structural
Identification of Fuzzy System ........................49
4.2.1 Topological Network .............................49
4.2.2 Improved SOFM by using Gaussian function.........52
4.3 Parameter identification of fuzzy system ..........56
4.4 Experimental results ..............................57
4.5 Conclusion ........................................61
Contents
III
Chapter 5
Adaptive Fuzzy Inference System for a Burn-In
System.................................................63
5.1 Introduction.......................................63
5.2 Burn-In test system................................64
5.2.1 Traditional Burn-In test system .................64
5.2.2 Improved Burn-In test system ....................65
5.3 Temperature control system for Burn-In test........67
5.3.2 Fuzzy temperature control system.................69
5.3.3 Adaptive fuzzy system design ....................75
5.3.3.1 Structural identification of fuzzy system......75
5.3.3.2 The parameter identification of fuzzy system ..77
5.4 Experimental results ..............................78
5.5 Conclusion ........................................85
Chapter 6
Conclusions............................................87
References.............................................89
Publications...........................................96
Patents................................................98

List of Figures
Figure 2.1 Difference between Manhattan distance and Euclidean distance. (a) Manhattan distance, (b) Euclidean distance. 7
Figure 2.2 Examples of distance functions. (a) Euclidean distance, (b) Manhattan distance, (c) Minkowski distance (p = 5), (d) Minkowski distance (p = 200). 8
Figure 2.3 Dendrogram for hierarchical clustering. 10
Figure 2.4 Relationship between divisive and agglomerative hierarchical clustering algorithms. 11
Figure 2.5 K-means clustering algorithm. 14
Figure 2.6 Examples of distance functions. (a) Euclidean distance (for spherical cluster), (b) the proposed distance (for spherical cluster), (c) Euclidean distance (for ellipsoidal cluster), (d) the proposed distance (for ellipsoidal cluster). 18
Figure 2.7 Simulations comparison for FCM and NFCM methods. (a) FCM (no outlier), (b) FCM (no outlier), (c) NFCM (no outlier), (d) FCM (added outlier), (e) NFCM (added outlier), (f) FCM (1st dimension is scaled with 5), (g) NFCM (1st dimension is scaled with 5). 20
Figure 2.8 Biological neuron. 25
Figure 2.9 Artificial neuron structures. 26
Figure 2.10 Common nonlinear functions used for synaptic inhibition. (a) Hard Limited, (b) tanh, (c) Sigmoid, (d) unnamed. 27
Figure 2.11 Output of a simple neuron as a function of its input ( ). (a) Hard Limited , (b) tanh, (c) Sigmoid, (d) unnamed. 28
Figure 2.12 Weight space (use sigmoid function). (a) , (b) , (c) =(2,2), (d) =(2,4), (e) =(-2,3), (f) =(1,-5). 29
Figure 2.13 Multilayer perceptrons. 32
Figure 2.14 (a) A one-dimensional sigmoidal RBF with center = 2 and smoothing parameter = 3. (b) A two-dimensional sigmoidal RBF with center vector = [2,2] and smoothing parameter = 3. 34
Figure 2.15 Architecture of Radial basis function network. 35
Figure 3.1 (a) Value of membership function of Hard c-means, (b) Value of membership function of Fuzzy c-means. 39
Figure 3.2 Automatic clustering with five clustering distribution data set. (a) Two dimensional data set of 850 data points. (b) iter=1, for a total of 296 clusters. (c) iter=3, for a total of 36 clusters. (d) iter=7, for a total of 5 clusters. 41
Figure 3.3 Fuzzy modeling of nonlinear system of single input and single output. (a) Nonlinear data set. (b) Rules=8, MSE = 0.0036. (c) Rules=18, MSE = 0.0006. 43
Figure 3.4 Fuzzy model construction result of normalized data of closing weighting stock index average and transaction quantity (thousands of stocks) in Taiwan’s stock market. (a) The original data of Taiwan’s stock market. (b) Rules=12, MSE = 0.0014. (c) Rules=23, MSE = 0.00065. 44
Figure 3.5 Fuzzy model construction result of nonlinear system of two inputs and one output. (a) The input-output data of nonlinear system of two inputs and one output. (b) Rules=8, MSE=0.0083. (c) Rules=12, MSE=0.00099. 46
Figure 4.1 Weight vector adjustment of the winning neuron. 50
Figure 4.2 SOFM architecture. 51
Figure 4.3 Neighborhood function. (a) Rectangular topology, (b) Hexagonal topology. 51
Figure 4.4 Phenomenon of neighborhood learning of neuron of output layer. 52
Figure 4.5 (a) 2D Gaussian distribution data set, (b) [1X14] one dimensional SOFM output, (c) [1X10] one dimensional SOFM output, (d) [1X8] one dimensional SOFM output. 54
Figure 4.6 (a) 3D Gaussian distribution data set, (b) [8X8] two dimensional SOFM output, (c) [5X5] two dimensional SOFM output, (d) [4X4] two dimensional SOFM output. 55
Figure 4.7 Input output data of nonlinear system. 58
Figure 4.8 Sinc(x1,x2) nonlinear system identification (l = 5, s = 6). (a) SOFM output (Φ = [5x6]), (b) nonlinear system structural identification, (c) fuzzy inference system output result, (d) error curved surface drawing (MSE = 2.9658x10-4). 58
Figure 4.9 Sinc(x1,x2) nonlinear system identification (l = 5, s = 8). (a) SOFM output (Φ = [5x8]), (b) nonlinear system structural identification, (c) fuzzy inference system output result, (d) error curved surface drawing (MSE = 7.8906x10-5). 60
Figure 5.1 Traditional Burn-In board. 65
Figure 5.2 Improved sockets installed in a Burn-In load board. 66
Figure 5.3 Block diagram of the traditional PI temperature controller for the Burn-In test. 69
Figure 5.4 Block diagram of the fuzzy temperature controller for the Burn-In test. 71
Figure 5.5 Membership functions of fuzzy controllers: (a) Membership functions of the error value ef of temperature. (b) Membership functions of the error eh of heater. (c) Membership functions of the error variation cef of fan. (d) Membership functions of the error variation ceh of heater.(e) Membership
functions of the voltage variation of ΔVf of fan. (f) Membership functions of
the voltage variation ΔVh of heater...................................................................75
Figure 5.6 NFCM system identification for Fan. (a) input-output surface of the 400
random data points, (b) nonlinear system structural identification, (c) error
curved surface drawing (MSE = 201.4219). ....................................................80
Figure 5.7 NFCM system identification for Heater. (a) input-output surface of the 400
random data points, (b) nonlinear system structural identification, (c) error
curved surface drawing (MSE = 233.7926). ....................................................81
Figure 5.8 Response time of the PI controller in the temperature control system.......83
Figure 5.9 Comparison chart of response time of the PI controller and fuzzy controller
in the temperature control system. ...................................................................83
Figure 5.10 Comparison chart of response time of the PI controller, fuzzy controller
and NFCM controller in the temperature control system. ...............................84
List of Tables
Table 2.1 Clustering results for FCM and NFCM. ......................................................21
Table 2.2 Activation functions commonly used in artificial neuron structure. ............26
Table 3.1 Parameter after system identification (experiment 2). .................................45
Table 3.2 Parameter after system identification (experiment 3). .................................47
Table 4.1 Parameter values obtained by the proposed method (l = 5, s = 6). ..............59
Table 4.2 Parameter values obtained by the proposed method (l = 5, s = 8). ..............60
Table 5.1 Fuzzy rule base of the fuzzy control of the fan............................................73
Table 5.2 Fuzzy rule base of the fuzzy control of the heater. ......................................73
Table 5.3 Parameter values obtained by the proposed method (r=6) for the fan. ........82
Table 5.4 Parameter values obtained by the proposed method (r=6) for the heater.....82
Table 5.5 The performance comparison between PI and fuzzy controller...................84
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