跳到主要內容

臺灣博碩士論文加值系統

(44.211.24.175) 您好!臺灣時間:2024/11/10 17:30
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:翁仲銘
研究生(外文):chung-ming own
論文名稱:智慧型模糊推論機制應用於時間序列分析和影像復原學習之設計
論文名稱(外文):The Design of Intelligent Fuzzy Inference Systems for Time Series Forecastand Image Restoration
指導教授:游寶達游寶達引用關係
指導教授(外文):Pao-Ta Yu
學位類別:博士
校院名稱:國立中正大學
系所名稱:通訊工程研究所
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:2005
畢業學年度:94
語文別:英文
論文頁數:179
中文關鍵詞:模糊推論系統型態二模糊集合模糊時間序列影像還原
外文關鍵詞:Fuzzy inference systemtype-2 fuzzy setsimage restoration
相關次數:
  • 被引用被引用:0
  • 點閱點閱:296
  • 評分評分:
  • 下載下載:55
  • 收藏至我的研究室書目清單書目收藏:1
日常生活中的釵h知識通常都是不完全的,甚至釵h意料之外的事更是無法完全詳細的陳述。因此人們常常想要追尋一種機制來讓他們可以根據現有的一些片段知識來獲得所要的結果。查德(Zadeh)是第一位根據模糊集合和模糊推論來提出近似推論的人。到目前為止,雖然模糊推論這方面的研究已經獲得廣泛的支持和成果,但是推論的結構仍然十分受到限制。在本論文中,作者提出了智慧型模糊推論機制,來嘗試改善根深蒂固的模糊推論結構,並獲得重要的成果。
首先在本論文中,提出了對解模糊機制的改善。一般來說,解模糊機制多受限於推論的結果。然而,本論文中提出的動態解模糊機制(adaptive defuzzification)試著在過去的推論資料中找出各個模糊值(memberships)的權重,並且根據權重,來動態調整解模糊過程中的模糊值。動態解模糊機制的好處是符合推論上的直覺和簡單性。並且在實際的案例探討上,驗證了動態模糊機制除了能夠反映時間序列的變動外,也在時間序列推測上獲得更好的正確性。
另外,因為型態一(type-1)模糊集合的模糊函數都是crisp值,無法直接處理推論上的不確定性(uncertainty)。相反地,型態二(type-2)模糊集合的模糊函數本身就是一個模糊集合,並且因為本身提供了更高階的自由度(freedom),因此夠直接塑模(Model)和解決推論上的不確定性。因此,在智慧型推論機制的第二部分,也就是高階的模糊推論機制,就是利用型態二模糊集合的特性,並且把模糊相似性拓展到型態二模糊集合推論上。另外,整個推論的結果也運用在影像復原和雜訊去除的處理上。在實驗的結果上,不僅所提出的模組可以維持影像的銳利度外,也能有效的去除雜訊。
Since much knowledge about many things and events in our daily life is necessarily incomplete, and since exceptions cannot be explicitly stated, people seek a mechanism that allows them to reach conclusions from incomplete information. Zadeh first proposed the concept of approximate reasoning based on the theory of fuzzy sets and fuzzy logic [1]. Although researchers have done many excellent works on the improvement of the rule based fuzzy inference system, the fundamentally new is still limited. In this thesis, the author proposed the novel models to improve the system performance by modifying the deep-rooted fuzzy inference structure.
This thesis first considers the improvement of the defuzzification. Generally, the research on defuzzification is limited and constrained by the inference results. Hence, the proposed model of adaptive defuzzification specifies the derived memberships as the weight to allocate the memberships on the defuzzification. The advantage of the adaptive defuzzification lies in its intuitive plausibility and computational simplicity. Empirical analyses of the forecasting of a futures index reveal that the proposed model reflects fluctuations in fuzzy time series and forecasts results more accurately than the previous models.
Consequently, since type-1 fuzzy sets are not able to directly model uncertainties because their membership functions are totally crisp. Conversely, type-2 fuzzy sets not only are able to model uncertainties because their membership functions are themselves fuzzy, but also provide additional degrees of freedom that make it possible to directly model uncertainties. Hence, the second aspect of intelligent fuzzy inference model is to extend the similarity reasoning on the type-2 fuzzy sets, which is the proposed high-level fuzzy inference model. Accordingly, the proposed model supplies another well-defined inference method on type-2 inference system, and employs the novel similarity measure on type-2 fuzzy sets. Besides, the proposed method applies in the image restoration to provide the useful information on case study. Extensive simulation results reveal that the proposed filter not only exhibits desirable robustness in suppressing noise but also outperforms other proposed filtering approaches.
Contents IX
List of Figures XI
List of Tables XIII
Chapter 1 Introduction 1
1.1 The Intelligent Fuzzy Inference Model 2
1.2 Contributions 4
1.3 Organization of the Thesis 12
Chapter 2 Related Work 13
2.1 Fuzzy Time Series 13
2.2 Type-2 Fuzzy Sets 21
Chapter 3 Weighted Heuristic Model 30
3.1 System Model and Definitions 31
3.2 Performance Evaluation 37
3.2.1 Analyses of Enrollment of University Alabama 37
3.2.2 Forecasts with Robust Capability 46
3.2.3 Analyses of TAIFEX 47
3.2.4 Experimental Results 55
Chapter 4 Heuristic High-Order Model 59
4.1 System Model and Definitions 60
4.2 Performance Evaluation 64
4.2.1 Analyses of TAIFEX 65
4.2.2 Experimental Results 72
Chapter 5 Type-2 Similarity Inference Model 75
5.1 Type-2 Similarity 75
5.2 System Design and Definition 78
5.3 Truth-Qualified Proposition 82
5.3.1 Case Study 86
Chapter 6 A2FM filter for Image Restoration 90
6.1 System Model and Definitions of FM Filter 90
6.2 Design of Type-2 FLS 96
6.2.1 Inference Process of the Type-2 FLS 97
6.2.2 Type Reduction and Defuzzification 99
6.2.3 Design of Interval Type-2 FLS 100
6.3 Performance Evaluation 105
Chapter 7 Fuzzy Ability Estimator 114
7.1 Basic Definitions of Computer Adaptive Test 115
7.2 Item Response Theory 118
7.3 Intelligent Computerized Adaptive Test 124
7.4 Performance Evaluation 129
7.4.1 Parameter Calibration Experiment 132
7.4.2 Stable Experiment 133
Chapter 8 Summary and Future Work 135
8.1 Future Work 138
Appendix A 140
Appendix B 146
B.1 Derivation of Uncertain Mean in Antecedent Sets 147
B.2 Derivation of Uncertain Mean in Consequent Sets 148
B.3 Derivation of Uncertain Deviation in Antecedent Sets 149
B.4 Derivation of Uncertain Deviation in Consequent Sets 151
Vita 153
Publications 154
Bibliography 157
[1]L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 338-353, 1965.
[2]L. A. Zadeh, “Theory of approximate reasoning,” Machine Intelligence, pp. 149-194, 1970.
[3]M. Mizumoto, “Approximate reasoning in expert system,” Extended Fuzzy Reasoning, vol. 71-85, 1985.
[4]C. C. Lee, “Fuzzy logic in control system: fuzzy logic controller- parts I&II,” IEEE Transaction on Systems, Man and Cybernetic, vol. 20, pp. 404-435, 1990.
[5]L.-X. Wang, A course in fuzzy systems and control, Prentice-Hall, Inc, 1997.
[6]L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning – I”, Information Science, vol. 8, pp. 199-249, 1975.
[7]J. M. Mendel and R. John, “Type-2 fuzzy sets made simple,” IEEE Transactions on Fuzzy Systems, vol. 10, pp. 117-127, 2002.
[8]N. N. Karnik and J. Mendel, “Applications of type-2 fuzzy logic systems to forecasting of time-series,” Information Sciences, vol. 120, pp. 89-111, 1999.
[9]Q. Liang and J. Mendel, “Equalization of nonlinear time-vary channels using type-2 fuzzy adaptive filters,” IEEE Transactions of Fuzzy Systems, vol. 8, pp. 551-563, 2000.
[10] I. Turksen, “Type-2 representation and reasoning for CWW,” Fuzzy Sets and Systems, vol. 127, pp. 17-36, 2002.
[11] I. Turksen, “Interval valued fuzzy sets based on normal forms,” Fuzzy Sets and Systems, vol. 20, pp. 191-210, 1986.
[12] I. Turksen, “Four methods of approximate reasoning with interval-valued fuzzy sets,” International Journal Approximate Reasoning, vol. 3, pp. 121-142, 1989.
[13] R. R. Yager, “Fuzzy subsets of type in decision,” Journal of Cybernetics, vol 10, pp. 137-159, 1980.
[14] M. Wagenknecht, K. Hartmann, “Application of fuzzy sets of type 2 to the solution of fuzzy equation systems,” Fuzzy Sets and Systems, vol. 25, pp.183-190, 1988.
[15] P. Diamond, “Higher level fuzzy numbers arising from fuzzy regression models,” Fuzzy Sets and Systems, vol. 36, pp. 265-275, 1990.
[16] I. Turksen, “Measurement of membership functions and their acquisition,” Fuzzy Sets and Systems, vol. 40, pp. 5-38, 1991.
[17] J. M. Mendel, “Computing with words when words can mean different things to different people,” International ICSC Congress Computation Intelligent: Methods Application. 3rd Annu. Symp. Fuzzy Logic Application, NY, 1999.
[18] S. Raha and K.S. Ray, “Reasoning with vague truth,” Fuzzy Sets and Systems, vol. 105, pp. 385-399, 1999.
[19] S. Raha and K.S. Ray, “Reasoning with vague default,” Fuzzy Sets and Systems, vol. 91, pp. 327-338, 1997.
[20] S. Raha and K.S. Ray, “Approximate reasoning with time,” Fuzzy Sets and Systems, vol. 107, pp. 59-79, 2000.
[21] N. N. Karnik, J. M. Mendel, “An introduction to type-2 fuzzy logic systems”, Proc. 1998 IEEE Fuzzy Conference, pp. 915-920, Anchorage, AK. MAY, 1998a.
[22] N. N. Karnik, J. M. Mendel, “Applications of Type-2 fuzzy logic systems to forecasting of time-series”, Information Sciences, vol. 120, pp. 89-111, 1999a.
[23] R. Zwick, E. Carlstein, and D. V. Budescu, “Measures of similarity among fuzzy concepts: a comparative analysis”, International Journal Approximate Reasoning, vol. 1, pp. 221-242, 1987.
[24] R. Sun, “Robust reasoning: integrating rule-based and similarity-based reasoning,” Artificial Intelligence, vol. 75, no. 2, pp. 241-295(55), 1995.
[25] F. Esteva, P. Garcia and L. Godo, Similarity-based reasoning, Physica-Verlag GmbH, Heidelberg, Germany, 2000.
[26] R. Zwick, E. Carlstein, and D. V. Budescu, “Measures of similarity among fuzzy concepts: a comparative analysis,” International Journal Approximate Reasoning., vol. 1, pp. 221-242, 1987.
[27] S. Raha, N. R. Pal and K. Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets and Systems, vol. 54, pp. 269-277, 1993.
[28] Q. Song and B. S. Chissom, “Forecasting Enrollments with Fuzzy time series- part II,” Fuzzy Sets and Systems, vol. 62 pp. 1-8, 1994.
[29] J. Sullivan and W. H. Woodall, “A comparison of fuzzy forecasting and markov modeling,” Fuzzy Sets and Systems, vol. 64, pp. 279-293, 1994.
[30] S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets and Systems, vol. 81 pp. 311-319, 1996.
[31] J.-R. Hwang, S.-M. Chen, and C.-H. Lee, "Handling forecasting problems using fuzzy time series," Fuzzy Sets and Systems, vol. 100 pp. 217-228, 1998.
[32] K. Huarng, “Heuristic models of fuzzy time series for forecasting,” Fuzzy Sets and Systems, vol. 123, pp. 369-386, 2001.
[33] S.-M. Chen, “Forecasting enrollments based on high-order fuzzy time series.” Cybernetics and Systems, vol. 33, pp.1-16, 2002.
[34] J. M. Mendel, “Uncertainty, fuzzy logic, and signal processing,” Signal Processing, vol. 80, pp. 913-933, 2000.
[35] H.-H. Tsai and P.-T. Yu, “Adaptive fuzzy hybrid multichannel filters for removal of impulsive noise from color images”, Signal Processing, vol. 7, pp. 127-151, 1999.
[36]T. Chen and H. R. Wu: 'Space variant median filters for the restoration of impulsive noise corrupted images', IEEE Trans. Circuits Syst. –II: Analog and Digital Signal Processing, vol. 48, pp. 784-789, 2001.
[37] H.-H. Tsai, S.-H. Chen, and P.-T. Yu, “On the design of neuro-fuzzy hybrid multichannel filters for color image restoration,” Journal of Electron Imaging, vol. 9, no.2, pp. 117-139, 2000.
[38] L. Yin, R. Yang, M. Gabbouj and Y. Neuvo, “Weighted median filters: a tutorial,” IEEE Transaction on circuits System, vol.11, no. 43, pp.157-192, 1996.
[39] S. J. Ko and Y. H. Lee, “Center weighted median filters and their applications to image enhancement,” IEEE Transaction on Circuits System, vol. 38, pp. 984-993, 1991..
[40] T. Sun and Y. Neuvo, “Detail-preserving median based filters in image processing,” Pattern Recognition Lett., vol. 15, pp. 341-347, 1994.
[41] D. A. F. Florencio and R. W. Schafer, “Decision-based median filter using local signal statistics,” in Proc. SPIE Symp., Visual Communication Image Processing, vol. 2038, pp. 268-275, 1994.
[42] T. Chen, K. K. Ma and L. H. Chen, “Tri-state median filter for image denoting,” IEEE Transaction on Image Process, vol. 8, pp. 1834-1838, 1999.
[43] T. Chen and H. R. Wu, “Impulse noise removal by multi-state median filtering,” in Proceeding IEEE International. Conference Acoustics, Speech, Signal Process, vol. IV, pp. 2183-2186, 2000.
[44] E. Abreu and S. K. Mitra, “A signal-dependent rank order mean (SD-ROM) filter. A new approach for removal of impulses from highly corrupted images,” in Proceeding. IEEE ICASSP-95, Detroit MI, pp. 2371-2374, 1995.
[45] A. K. Jain, Fundamentals of Digital Image Processing, Englewood Cliffs, NJ: Prentice-Hall, 1989.
[46] T. Chen and H. Wu, “Adaptive impulsive detection using center-weighted median filters,” Signal Processing Lett. vol. 8, pp. 1-3, 2001.
[47] T. Sun and Y. Neuvo, “Detail-preserving median based filters in image processing,” Pattern Recognition Lett., vol. 15, pp. 341-347, 1994.
[48] P. M. Embree and B. Kimble, C Language Algorithms for Digital Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, 1991.
[49] S. Zhang and A. Karimm, “A new impulse detector for switching median filter,” IEEE Signal Process Lett., vol. 9, pp. 360-363, 2002.
[50] H. G. Senel, R. A. Peters and B. Dawant, “Topological median filters,” IEEE Transaction on Image Process, vol. 11, pp. 89-104, 2002.
[51]M. Emin Yuksel and Erkan Besdok: 'A simple neuro fuzzy impulse detector for efficient blur reduction fo impulse noise removal operators for digital images', IEEE Trans. Fuzzy Syst., vol. 12, pp. 854-865, 2004.
[52]G. Pok, Jyh-Charn Liu and A. S. Nair, “Selective removal of impulse noise based on homogeneity level information,” IEEE Transactions on Image Process, vol. 12, 85-92, 2003.
[53]X. Xu, E.L. Miller, D. Chen and M. Sarhadi, “Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images,” IEEE Transaction Image Process, vol. 13, pp. 238-247, 2004.
[54] K. Arakawa, “Median filter based on fuzzy rules and its application to image restoration,” Fuzzy Sets and System, vol. 77, pp. 3-13, 1996.
[55] R. Kruse, J. Gebhardt & F. Klawonn, Foundations of Fuzzy Systems, John Wiley & Sons, 1994.
[56] W. J. van der Lindan and A. R.K. Hambleton, Handbook of Modern Item Response Theory, London: Springer Verlag, 1997.
[57] K. H. Ronald, H. Swaminathan and H. J. Rogers, Fundamentals of Item Response Theory, Sage Publications, 1991.
[58] R. D. Hays, L. S. Morales and S. Reise, “Item response theory and health outcomes measurement in the 21st century,” Medical Care, vol. 38, pp. 28-42, 2000.
[59] Q. Song and B. S. Chissom, “Forecasting enrollments with fuzzy time series- part I,” Fuzzy Sets and Systems, vol. 54 pp. 1-9, 1993.
[60] Q. Liang and J.M. Mendel, “Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters,” IEEE transactions on fuzzy systems, vol. 8, pp. 551-563, 2000.
[61] J. M. Mendel, “Computing with words when words can mean different things to different people,” Int. ICSC Congress Computation Intelligence: Methods Application. 3rd Annu. Symp. Fuzzy Logic Application, NY, 1999.
[62] D. Dubois and H. Prade, “Operation on fuzzy numbers,” Int. Journal System Science, vol. 9, pp. 613-626, 1978.
[63] F. C. C. Tsang, J.W.T. Lee and D.S. Yeung, “Similarity based fuzzy reasoning methods for fuzzy production rules,” Proceeding IFSA, World Congress, Sao Paolo Brazil , pp. 157-160, 1995.
[64] I. B. Turksen and Z. Zhong, “An approximate analogical reasoning approach based on similarity measures,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 18, no. 6, pp. 1049-1056, 1988.
[65] S.-M. Chen, “A new approach to handling fuzzy decision making problems,” IEEE transactions on Systems, Man, Cybernetics, vol. 18, no. 6, pp. 1012-1016, 1988.
[66] S.-M. Chen, “A weighted fuzzy reasoning algorithm for medical diagnosis,” Decision support system, vol. 11, pp. 37-43, 1994.
[67] D. S. Yeung and E.C.C. Tsang, “Improved fuzzy knowledge representation and rule evaluation using fuzzy petri nets and degree of subsethood,” Intelligent System, vol. 9, pp. 1083-1100, 1994.
[68] M. Emin Yuksel and E. Besdok, “A simple neuro fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images,” IEEE Transaction on Fuzzy System, vol. 12, pp. 854-865, 2004.
[69] T. Chen and H. R. Wu, “Space variant median filters for the restoration of impulsive noise corrupted images,” IEEE Transaction on Circuits System –II: Analog and Digital Signal Processing, vol. 48, pp. 784-789, 2001.
[70] S. Russell and P. Norvig, Aritificial intelligence: a modern approach. Prentice-Hall, Englewood Cliffs, NJ, 1995.
[71] C. P. Pappis and N. I. Karacapilidis, “A comparative assessment of measures of similarity of fuzzy values,” Fuzzy Sets and Systems, vol. 56, pp. 171-174, 1993.
[72] H. Wainer, Computerized adaptive Testing, Lawrence Erlbaum Associates, 2000.
[73] M. D. Shermis, H. R. Mzumara, and S. T. Bublitz, “On test and computer anxiety: test performance under CAT and SAT conditions,” Journal of Educational Computing Research, pp. 57-75, 2001.
[74] S. L. Wise and G. G. Kingsbury, “Pratical issues in developoing and maintaing a computerized adaptive testing program,” Psicologica, pp. 35-155, 2000.
[75] W. J. van der Linden and G. A. W. Glas, “Capitalization on item calibration error in adaptive testing,” Applied Measurement in Education, vol. 13, pp. 35-53, 2000.
[76] D. Thissen, R. J. Mialevy and H. Wainer, Computerized Adaptive Testing: a Primer, pp. 103-135, Hillsdale, NJ:Erlbaum, 1990.
[77] L. S. Wang, “A simulation study of variable item selection in computerized adaptive testing,” Proceeding s 1990 Taipei Symposium in Statistics, pp. 575-604, 1990.
[78] R.K Hambleton, H. Swaminathan, Item Response Theory Principles and Applications, MA:Academic, 1985.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top