( 您好!臺灣時間:2021/08/02 00:32
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::


論文名稱(外文):A Fuzzy Decision-Making Approach to Cause-Effect Modeling
指導教授(外文):Sheng-Tun Li
外文關鍵詞:Cause-effect relationshipsFuzzy cognitive mapsHebbian learningFuzzy transitive closure
  • 被引用被引用:0
  • 點閱點閱:163
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
It is not a simple task to depict cause and effect relations in a complex systems, especially they are always dynamic and constantly changing. However, this task is very critical to achieve optimum and balanced status in any situation. Nowadays, there are a lot of tools for the expression and analysis of the relationship of causes and effects in these systems. These methods or tools have been developed to assist this, and they have been used widely and effectively in various fields.
This study utilizes fuzzy cognitive maps (FCMs) to represent the cause and effect relationships in a complex system. Since fuzzy cognitive maps have some limitations, such as using fixed weights when the system changes, examining direct influences only, and so on, we use Hebbian learning, fuzzy transitive closure and a convergence method to overcome these. We get much better performance from adjusting the weight matrix using this approach. Furthermore, we illustrate the use of this method with real medical data, and predict the probability of getting a stroke using fuzzy cognitive maps. It is anticipated that this can provide extra information to doctors or patients with regard to the health status of the latter.
摘 要 I
Abstract II
誌 謝 III
Table of Contents IV
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background and research motivations 1
1.2 Research objectives 3
1.3 The process of the research 4
Chapter 2 Literature review 5
2.1 Fuzzy theory 5
2.1.1 Fuzzy set and fuzzy set theory 5
2.1.2 Fuzzy relation 6
2.2 Fuzzy cognitive map 7
2.2.1 Causal network methodology 7
2.2.2 Fuzzy cognitive map 8
2.2.3 Applications of fuzzy cognitive map 13
2.3 Learning methods 16
2.3.1 Unsupervised learning methods 18
2.3.2 Learning methods for FCMs 19
2.3.3 The active hebbian learning algotithm 20
Chapter 3 Research method 24
3.1 Constructing fuzzy cognitive maps 25
3.1.1 Formalization of fuzzy cognitive maps 25
3.1.2 Construction process 27
3.2 Hebbian learning 29
3.2.1 Learning goal 29
3.2.2 Learning algorithm and process 29
3.3 Fuzzy transitive closure 31
3.4 Convergence Method 32
Chapter 4 Experiment and Analysis 33
4.1 Stabilize the weight matrix for FCM 33
4.2 Fuzzy cognitive map model for stroke 37
Chapter 5 Conclusion and future work 43
5.1 Conclusion 43
5.2 Future work 44
References 45
Abebe, A. J., Guinot, V., & Solomatine, D. P. (2000). Fuzzy alpha-cut vs. Monte Carlo techniques in assessing uncertainty in model parameters. Paper presented at the proceeding of the 4th conference on Hydroinformatics. Iowa , USA .
Alizadeh, S., & Ghazanfari, M. (2009). Learning FCM by chaotic simulated annealing. Chaos Solitons & Fractals, 41(3), 1182-1190. doi: 10.1016/j.chaos.2008.04.058
Andreou, A. S., Mateou, N. H., & Zombanakis, G. A. (2005). Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps. Soft Computing, 9(3), 194-210. doi: 10.1007/s00500-004-0344-0
Beeson, P., Modayil, J., & Kuipers, B. (2010). Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy. International Journal of Robotics Research, 29(4), 428-459. doi: 10.1177/0278364909100586
Bertolini, M. (2007). Assessment of human reliability factors: A fuzzy cognitive maps approach. International Journal of Industrial Ergonomics, 37(5), 405-413. doi: 10.1016/j.ergon.2005.12.009
Bueno, S., & Salmeron, J. L. (2008). Fuzzy modeling Enterprise Resource Planning tool selection. Computer Standards & Interfaces, 30(3), 137-147. doi: 10.1016/j.csi.2007.08.001
Bueno, S., & Salmeron, J. L. (2009). Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications, 36(3, Part 1), 5221-5229. doi: http://dx.doi.org/10.1016/j.eswa.2008.06.072
Coban, O., & Secme, G. (2005). Prediction of socio-economical consequences of privatization at the firm level with fuzzy cognitive mapping. Information Sciences, 169(1-2), 131-154. doi: 10.1016/j.ins.2004.02.009
Dickerson, J.A. & Kosko, B. (1993). Virtual worlds as fuzzy cognitive maps, in Proc. IEEE Virtual Reality Annu. Int. Symp., 417-477, New York, NY.
Dickerson, J.A., & Kosko, B. (1997). Virtual worlds as fuzzy cognitive maps. Fuzzy Engineering, 125-141.
Eden, C., Ackermann, F., & Cropper, S. (1992). The analysis of cause maps. Journal of Management Studies, 29(3), 309-324.
Elf, M., Putilova, M., von Koch, L., & Öhrn, K. (2007). Using system dynamics for collaborative design: a case study. BMC Health Services Research, 7, 123-134.

Fairweather, J. (2010). Farmer models of socio-ecologic systems: Application of causal mapping across multiple locations. Ecological Modelling, 221(3), 555-562. doi: 10.1016/j.ecolmodel.2009.10.026
Froelich, W., & Wakulicz-Deja, A. (2009). Mining temporal medical data using adaptive fuzzy cognitive maps. Proceedings - 2009 2nd Conference on Human System Interactions, HSI '09, art. no. 5090946, 16-23.
Furfaro, R., Kargel, J. S., Lunine, J. I., Fink, W., & Bishop, M. P. (2010). Identification of cryovolcanism on Titan using fuzzy cognitive maps. Planetary and Space Science, 58(5), 761-779. doi: 10.1016/j.pss.2009.12.003
Georgopoulos, V. C., Malandraki, G. A., & Stylios, C. D. (2003). A fuzzy cognitive map approach to differential diagnosis of specific language impairment. Artificial Intelligence in Medicine, 29(3), 261-278. doi: 10.1016/s0933-3657(02)00076-3
Gettings, M. E., Bultman, M. W., & Fisher, F. S. (2004). A complex systems model approach to quantified mineral resource appraisal. Environmental Management, 33(1), 87-98. doi: 10.1007/s00267-003-2835-7
Ghazanfari, M., Alizadeh, S., Fathian, M., & Koulouriotis, D. E. (2007). Comparing simulated annealing and genetic algorithm in learning FCM. Applied Mathematics and Computation, 192(1), 56-68. doi: 10.1016/j.amc.2007.02.144
Glies, B. G., Findlay, C. S., Haas, G., LaFrance, B., Laughing, W., & Pembleton, S. (2007). Integrating conventional science and aboriginal perspectives on diabetes using fuzzy cognitive maps. Social Science & Medicine, 64(3), 562-576. doi: 10.1016/j.socscimed.2006.09.007
Gonzalez, J. L., Aguilar, L. T., & Castillo, O. (2009). A Cognitive Map and Fuzzy Inference Engine Model for Online Design and Self Fine-Tuning of Fuzzy Logic Controllers. International Journal of Intelligent Systems, 24(11), 1134-1173. doi: 10.1002/int.20379
Hai, Z. G., & Luo, X. F. (2006). Automatic generation of document semantics for the e-science Knowledge Grid. Journal of Systems and Software, 79(7), 969-983. doi: 10.1016/j.jss.2005.08.022
Huerga, A. V. (2002). A balanced differential learning algorithm in fuzzy cognitive maps. This paper presented at the 16th Int. Workshop Qualitat. Reason.,Catalonia, Spain.
Innocent, P. R., & John, R. I. (2004). Computer aided fuzzy medical diagnosis. Information Sciences, 162(2), 81-104. doi: 10.1016/j.ins.2004.03.003
Kang, I., Lee, S., & Choi, J. H. (2004). Using fuzzy cognitive map for the relationship management in airline service. Expert Systems with Applications, 26(4), 545-555. doi: 10.1016/j.eswa.2003.10.012
Konar, A., & Chakraborty, U. K. (2005). Reasoning and unsupervised learning in a fuzzy cognitive map. Information Sciences, 170(2-4), 419-441. doi: 10.1016/j.ins.2004.03.012
Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65-75. doi: 10.1016/s0020-7373(86)80040-2
Kosko, B. (1992). Fuzzy associative memory systems. Fuzzy Expert Systems. CRC Press, Boca Raton, FL, 135-162.
Kurgan, L. A., Stach, W., & Ruan, J. (2007). Novel scales based on hydrophobicity indices for secondary protein structure. Journal of Theoretical Biology, 248(2), 354-366. doi: 10.1016/j.jtbi.2007.05.017
Lai, X., Zhou, Y., & Zhang, W.. (2009, 26-28 Dec. 2009). Software Usability Improvement: Modeling, Training and Relativity Analysis. Paper presented at the Information Science and Engineering (ISISE), 2009 Second International Symposium on.
Langfield-Smith, K. (1992). Exploring the need for a shared cognitive map. Journal of Management Studies, 29(3), 349-368.
Laukkanen, M. (1994). Comparative cause mapping of organizational cognitions. Organization Science, 5(3), 322-343. doi: 10.1287/orsc.5.3.322
Lazzerini, B., & Mkrtchyan, L. (2011). Analyzing Risk Impact Factors Using Extended Fuzzy Cognitive Maps. Ieee Systems Journal, 5(2), 288-297. doi: 10.1109/jsyst.2011.2134730
Lee, K. C., & Lee, S. (2007). Causal knowledge-based design of EDI controls: an explorative study. Computers in Human Behavior, 23(1), 628-663. doi: 10.1016/j.chb.2004.11.003
Lee, S. J., Kim, B. G., & Lee, K. D. (2004). Fuzzy cognitive map-based approach to evaluate EDI performance: a test of causal model. Expert Systems with Applications, 27(2), 287-299. doi: 10.1016/j.eswa.2004.02.003
Lin, C.T., & Lee, C.S.G. (1996). Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice-Hall, Upper Saddle River, NJ.
Liu, Z. Q. (2003). Fuzzy cognitive maps in GIS data analysis. Soft Computing, 7(6), 394-401. doi: 10.1007/s00500-002-0228-0
Malina, M. A., & Selto, F. H. (2004). Choice and change of measures in performance measurement models. Management Accounting Research, 15(4), 441-469. doi: http://dx.doi.org/10.1016/j.mar.2004.08.002
Mendoza, G. A., & Prabhu, R. (2006). Participatory modeling and analysis for sustainable forest management: Overview of soft system dynamics models and applications. Forest Policy and Economics, 9(2), 179-196. doi: 10.1016/j.forpol.2005.06.006
Miao, C., Yang, Q., Fang, H., & Goh, A. (2007). A cognitive approach for agent-based personalized recommendation. Knowledge-Based Systems, 20(4), 397-405. doi: 10.1016/j.knosys.2006.06.006
Miles, M., & Huberman, A. (1991). Qualitative Data Analysis: A Source Book of New Methods. Sage, California, USA.
Nash, R. (2006). Causal network methodology - Tourism research applications. Annals of Tourism Research, 33(4), 918-938. doi: 10.1016/j.annals.2006.02.002
Ndousse, T.D. & Okuda, T. (1996). Computational intelligence for distributed fault management in networks using fuzzy cognitive maps. Proc. IEEE Int. Conf. Commun. Converging Technol. Tomorrow's Applicat., 1558-1562, New York.
Ozesmi, U., & Ozesmi, S. L. (2004). Ecological models based on people's knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1-2), 43-64. doi: 10.1016/j.ecolmodel.2003.10.027.
Pajares, G., & de la Cruz, J. M. (2006). Fuzzy cognitive maps for stereovision matching. Pattern Recognition, 39(11), 2101-2114. doi: 10.1016/j.patcog.2006.04.003.
Papageorgiou, E. I., & Groumpos, P. P. (2005b). A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps. Applied Soft Computing, 5(4), 409-431. doi: 10.1016/j.asoc.2004.08.008.
Papageorgiou, E. I., & Groumpos, P. P. (2005c). A weight adaptation method for fine-tuning fuzzy cognitive map causal links. Soft Computing, 9, 846-857.
Papageorgiou, E.I., Markinos, A.T., & Gemtos, T.A. (2010). Soft Computing Technique of Fuzzy Cognitive Maps to Connect Yield Defining Parameters with Yield in Cotton Crop Production in Central Greece as a Basis for a Decision Support System for Precision Agriculture Application. Fuzzy Cognitive Maps, 247, 325-362.
Papageorgiou, E.I., Papandrianos, N.I., Karagianni, G., Kyriazopoulos, G.C., & Sfyras, D. (2009). A fuzzy cognitive map based tool for prediction of infectious diseases. 2009 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009, art. no. 5277254, 2094-2099.
Papageorgiou, E. I., Spyridonos, P. P., Glotsos, D. Th, Stylios, C. D., Ravazoula, P., Nikiforidis, G. N., & Groumpos, P. P. (2008). Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Applied Soft Computing, 8(1), 820-828. doi: 10.1016/j.asoc.2007.06.006.
Papageorgiou, E. I., Spyridonos, P. P., Stylios, C. D., Ravazoula, P., Groumpos, P. P., & Nikiforidis, G. N. (2006). Advanced soft computing diagnosis method for tumour grading. Artificial Intelligence in Medicine, 36(1), 59-70. doi: 10.1016/j.artmed.2005.04.001.

Papageorgiou, E.I., Stylios, C.D., & Groumpos, P.P. (2001). Learning Algorithms For Fuzzy Cognitive Maps. 2nd International Conference in Fuzzy Logic and Technology, 83-88.
Papageorgiou, E., Stylios, C., & Groumpos, P. (2003). Fuzzy Cognitive Map learning based on nonlinear Hebbian rule. In T. D. Gedeon & L. C. C. Fung (Eds.), Ai 2003: Advances in Artificial Intelligence, 2903, 256-268.
Papageorgiou, E. I., Stylios, C.D., & Groumpos, P. P. (2006). Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. International Journal of Human-Computer Studies, 64(8), 727-743. doi: 10.1016/j.ijhcs.2006.02.009.
Papageorgiou, E.I., Stylios, C.D., & Groumpos, P.P. (2004). Active Hebbian learning algorithm to train fuzzy cognitive maps. International Journal of Approximate Reasoning, 37(3), 219-249.
Papageorgiou, E. I., & Froelich, W. (2012). Multi-step prediction of pulmonary infection with the use of evolutionary fuzzy cognitive maps. Neurocomputing, 92, 28-35. doi: 10.1016/j.neucom.2011.08.034
Parisi, C., & Hockerts, K.N. (2008). Managerial mindsets and performance measurement systems of CSR-related intangibles. Measuring Business Excellence, 12(2), 51-67.
Petalas, Y. G., Parsopoulos, K. E., & Vrahatis, M. N. (2009). Improving fuzzy cognitive maps learning through memetic particle swarm optimization. Soft Computing, 13(1), 77-94. doi: 10.1007/s00500-008-0311-2
Pelaez, C.E. & Bowles, J.B. (1995). Applying fuzzy cognitive maps knowledge- representation to failure modes effects analysis. Proc. IEEE Annu. Reliability Maintainability Symp, 450-456, New York, NY.
Rajaram, T., & Das, A. (2010). Modeling of interactions among sustainability components of an agro-ecosystem using local knowledge through cognitive mapping and fuzzy inference system. Expert Systems with Applications, 37(2), 1734-1744. doi: 10.1016/j.eswa.2009.07.035
Ramsey, D. S. L., & Norbury, G. L. (2009). Predicting the unexpected: using a qualitative model of a New Zealand dryland ecosystem to anticipate pest management outcomes. Austral Ecology, 34(4), 409-421. doi: 10.1111/j.1442-9993.2009.01942.x
Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling IT projects success with fuzzy cognitive maps. Expert Systems with Applications, 32(2), 543-559. doi: 10.1016/j.eswa.2006.01.032

Salmeron, J.L. (2009). Supporting decision makers with fuzzy cognitive maps: These extensions of cognitive maps can process uncertainty and hence improve decision making in R&D applications. Research Technology Management, 52(3), 53-59.
Sharif, A. M., & Irani, Z. (2006). Exploring Fuzzy Cognitive Mapping for IS Evaluation. European Journal of Operational Research, 173(3), 1175-1187. doi: 10.1016/j.ejor.2005.07.011
Skov, F., & Svenning, J. C. (2003). Predicting plant species richness in a managed forest. Forest Ecology and Management, 180(1-3), 583-593. doi: 10.1016/s0378-1127(02)00646-1
Song, Hengjie, Miao, Chunyan, Roel, Wuyts, Shen, Zhiqi, & Catthoor, Francky. (2010). Implementation of Fuzzy Cognitive Maps Based on Fuzzy Neural Network and Application in Prediction of Time Series. Ieee Transactions on Fuzzy Systems, 18(2), 233-250. doi: 10.1109/tfuzz.2009.2038371
Spector, J. M., Christensen, D. L., Sioutine, A. V., & McCormack, D. (2001). Models and simulations for learning in complex domains: using causal loop diagrams for assessment and evaluation. Computers in Human Behavior, 17(5-6), 517-545.
Stach, W., Kurgan, L., Pedrycz, W., & Reformat, M. (2005). Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems, 153(3), 371-401. doi: 10.1016/j.fss.2005.01.009
Stach, W., L.Kurgan, & Pedrycz, W. (2007). Parallel learning of large fuzzy cognitive maps. Proc. Int. Joint Conf. Neural Netw, 1584-1589.
Sterman, J. D. (2006). Learning from evidence in a complex world. American Journal of Public Health, 96(3), 505-514. doi: Doi 10.2105/Ajph.2005.066043
Stylios, C. D., & Groumpos, P. P. (2000). Fuzzy Cognitive Maps in modeling supervisory control systems. Journal of Intelligent & Fuzzy Systems, 8(2), 83-98.
Suwignjo, P., Bititci, U. S., & Carrie, A. S. (2000). Quantitative models for performance measurement system. International Journal of Production Economics, 64(1-3), 231-241. doi: 10.1016/s0925-5273(99)00061-4
Tsadiras, A. K., & Margaritis, K. G. (1997). Cognitive mapping and certainty neuron fuzzy cognitive maps. Information Sciences, 101(1-2), 109-130. doi: 10.1016/s0020-0255(97)00001-7
Wacker, J. G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16(4), 361-385. doi: http://dx.doi.org/10.1016/S0272-6963(98)00019-9
Xirogiannis, G., Stefanou, J., & Glykas, M. (2004). A fuzzy cognitive map approach to support urban design. Expert Systems with Applications, 26(2), 257-268. doi: 10.1016/s0957-4174(03)00140-4
Xirogiannis, G., & Glykas, M. (2007). Intelligent modeling of e-business maturity. Expert Systems with Applications, 32(2), 687-702. doi: 10.1016/j.eswa.2006.01.042
Yaman, D., & Polat, S. (2009). A fuzzy cognitive map approach for effect-based operations: An illustrative case. Information Sciences, 179(4), 382-403. doi: 10.1016/j.ins.2008.10.013
Yu, R., & Tzeng, G. H. (2006). A soft computing method for multi-criteria decision making with dependence and feedback. Applied Mathematics and Computation, 180(1), 63-75. doi: 10.1016/j.amc.2005.11.163
Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8(3), 338-353.
Zhu, Y., & Zhang, W. (2008). An integrated framework for learning fuzzy cognitive map using RCGA and NHL algorithm. This paper is presented at the Int. Conf. Wireless Commun., Netw. Mobile Comput., Dalian, China.
電子全文 電子全文(網際網路公開日期:20231231)
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
第一頁 上一頁 下一頁 最後一頁 top