二型模糊系统理论及应用
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摘要
作为对传统模糊集合（一型模糊集合）理论的扩展，1975年Zadeh教授引入了二型模糊集合的概念。二型模糊集合最大的特点就是采用了三维的隶属度函数，使其集合元素的隶属度本身成为一个[0,1]间的模糊数。因此二型模糊集合理论更适用于存在多重不确定性的情况，例如：隶属度函数自身的形状或参数存在不确定性。一型模糊集合理论是传统集合理论的扩展，当一个元素不能完全属于或完全不属于某个集合时（即其属于某个集合的“程度”介于0与1之间时）我们选择一型模糊集合。进一步的，当一个元素属于某个集合的“程度”也不确定时（即该元素属于某个集合的隶属度值也不确定时），我们就可以选择二型模糊集合。
模糊集合理论的应用大部分都集中在模糊系统上。模糊系统是以模糊集合理论为基础的一种基于知识或规则的系统，其核心是由IF-THEN规则组成的知识库。二型模糊系统与传统的一型模糊系统相类似，也是通过组合IF-THEN规则构造而成。但因为其理论基础为二型模糊集合理论，所以二型模糊系统较一型模糊系统而言，可以直接掌控更多的不确定性信息。由于二型模糊集合的运算复杂度较高，目前大多数基于二型模糊集合理论的应用都选择采用区间二型模糊集合。本论文通过构建区间二型模糊系统模型，着重研究了二型模糊系统理论在自动化控制领域和模式识别领域的应用。主要内容为：
1．构建了离散区间二型T-S模糊系统模型，采用并行分布式补偿法设计出了针对该模型的模糊控制系统。利用李亚普诺夫方法，以线性矩阵不等式的形式给出了该系统稳定工作的约束条件，并用数值仿真验证了该约束条件的有效性。通过颠簸路面上自主平行泊车控制系统的应用实例，详细介绍了该离散二型T-S模糊控制系统的设计过程，验证了二型模糊系统在掌控多重不确定性信息方面的优势。
2．基于具有时延特性的连续区间二型T-S模糊系统模型，采用并行分布式补偿法设计出相应的模糊控制系统。利用李亚普诺夫方法，以线性矩阵不等式的形式给出了系统稳定工作的约束条件，并用数值仿真验证了该控制系统的有效性。以连续搅拌槽反应过程控制为例，详细介绍了具有时延特性的连续区间二型T-S模糊控制系统的设计过程，并进一步验证了二型模糊系统在掌控多重不确定信息方面较一型模糊系统所具有的优势。
3．针对中文自动摘要问题，提出了一种基于二型模糊C均值聚类算法的摘要自动提取方案。该方案以中文句子为单位，通过计算中心句特征、标题特征、高频词特征、首句特征、句子长度、句子位置、数量词特征构建特征向量，对文本内容进行聚类分析，找出文本内容中的关键句来构建摘要。最后通过与采用一型模糊C均值算法的自动摘要结果相对比，验证了基于二型模糊集合的摘要算法比基于一型模糊集合的摘要算法获得的摘要结果更准确。
4．针对数字音频信号分类问题，提出了基于二型模糊集合理论的C均值聚类算法，在此基础上应用跳跃基因遗传算法对聚类得到的初始模糊模型进行优化，并采用向量相似性测度准则对优化后的模糊规则进行简化，得到最终的模糊分类器模型。通过实例仿真验证了采用二型模糊C均值聚类算法的音频信号分类器比采用一型模糊C均值聚类算法的分类器得到的分类结果更准确。
In1975, Professor Zadeh introduced the type-2fuzzy set to expand the traditionalfuzzy set (type-1fuzzy set). Compared with the type-1fuzzy set, the most advantage ofthe type-2fuzzy set is to use three-dimensional membership functions, so themembership degree of the element becomes a fuzzy number in [01]. The type-2fuzzysets theory is more applicable to the case of the multi-uncertainty. For example, theshape or parameters of membership functions are uncertain. When an element can notbe entirely part of a set (the membership of the element that belongs to a set is between0and1), we choose type-1fuzzy sets. When the “membership” of an element belongsto a set is uncertain (the membership degree of an element is uncertain), we can choosethe type-2fuzzy sets.
Now, fuzzy systems have become most successful in the applications of fuzzy setstheory. The core of a fuzzy system is the knowledge database formed by fuzzyIF-THEN rules, which is based on the fuzzy sets theory. The same as the type-1fuzzysystem, type-2fuzzy system is also constructed by the combination of fuzzy IF-THENrules. However, the type-2fuzzy system is based on type-2fuzzy sets. Compared withthe type-1fuzzy system, the type-2fuzzy system can handle the multi-uncertaintydirectly. Because the high computational complexity of the type-2fuzzy sets, most ofthe applications of type-2fuzzy sets theory focus on the interval type-2fuzzy sets.Through constructing the model of interval type-2fuzzy systems, this PhD thesisstudies the applications of type-2fuzzy system in the field of automatic control andpattern recognition. The main contents are as follows:
1. Based on the discrete interval type-2T-S fuzzy model, a fuzzy control system isdesigned by using the parallel distributed compensation. By using the Lyapunovmethod, the stabilization of the control system is analyzed, and the results arepresented in terms of linear matrix inequalities. The effectiveness of the resultsis illustrated by numerical simulations. Finally, the type-2fuzzy controllerdesign for autonomous parallel parking on bumpy road is applied to detail the design processes. The simulation results have showed the advantages of type-2fuzzy control systems on handling the information of multi-uncertainty.
2. Based on the continued interval type-2T-S fuzzy model with time-varing delay,a fuzzy control system is designed by using the parallel distributedcompensation. By using the Lyapunov method, the stabilization of the controlsystem is analyzed, the results are presented in terms of linear matrixinequalities. The effectiveness of the control system is verified by numericalsimulations. Finally, the type-2fuzzy controller design for the continuous stirredtank reactor is discussed to detail the design process. The results of thesimulations further illustrate the advantages of type-2fuzzy control systems onhandling the information of multi-uncertainty.
3. Chinese automatic summarization method is discussed based on the type-2fuzzy C-means clustering. In order to use the cluster analysis method, it isnecessary to represent Chinese sentences as vectors of features. These featuresinclude sentence centrality, title feature, high frequency words feature, firstsentence feature, sentence length, sentence position, and numerical data feature.Based on the text clustering analysis, the key sentences of the text can be foundout to construct the summary. Finally, the experiments have showed that theChinese automatic summarization result is more accurate to adopt the type-2fuzzy system than to adopt the type-1fuzzy system.
4. Type-2fuzzy c-means clustering algorithm is applied to solve the digital audiosignal classification problem, and jumping genes genetic algorithm is used tooptimize the initial fuzzy model which is obtained by the clustering algorithm.At last, the optimized fuzzy rule base is simplified by the vector similaritymeasure, and the final fuzzy classifier model is obtained. Compared to theconventional type-1fuzzy sets, type-2fuzzy sets can handle more uncertaininformation. Especially for sample sets whose samples distribute uneven andstructures are irregular. The experiment results are more precise when the type-2fuzzy c-means clustering algorithm is adopted.
模糊集合理论的应用大部分都集中在模糊系统上。模糊系统是以模糊集合理论为基础的一种基于知识或规则的系统，其核心是由IF-THEN规则组成的知识库。二型模糊系统与传统的一型模糊系统相类似，也是通过组合IF-THEN规则构造而成。但因为其理论基础为二型模糊集合理论，所以二型模糊系统较一型模糊系统而言，可以直接掌控更多的不确定性信息。由于二型模糊集合的运算复杂度较高，目前大多数基于二型模糊集合理论的应用都选择采用区间二型模糊集合。本论文通过构建区间二型模糊系统模型，着重研究了二型模糊系统理论在自动化控制领域和模式识别领域的应用。主要内容为：
1．构建了离散区间二型T-S模糊系统模型，采用并行分布式补偿法设计出了针对该模型的模糊控制系统。利用李亚普诺夫方法，以线性矩阵不等式的形式给出了该系统稳定工作的约束条件，并用数值仿真验证了该约束条件的有效性。通过颠簸路面上自主平行泊车控制系统的应用实例，详细介绍了该离散二型T-S模糊控制系统的设计过程，验证了二型模糊系统在掌控多重不确定性信息方面的优势。
2．基于具有时延特性的连续区间二型T-S模糊系统模型，采用并行分布式补偿法设计出相应的模糊控制系统。利用李亚普诺夫方法，以线性矩阵不等式的形式给出了系统稳定工作的约束条件，并用数值仿真验证了该控制系统的有效性。以连续搅拌槽反应过程控制为例，详细介绍了具有时延特性的连续区间二型T-S模糊控制系统的设计过程，并进一步验证了二型模糊系统在掌控多重不确定信息方面较一型模糊系统所具有的优势。
3．针对中文自动摘要问题，提出了一种基于二型模糊C均值聚类算法的摘要自动提取方案。该方案以中文句子为单位，通过计算中心句特征、标题特征、高频词特征、首句特征、句子长度、句子位置、数量词特征构建特征向量，对文本内容进行聚类分析，找出文本内容中的关键句来构建摘要。最后通过与采用一型模糊C均值算法的自动摘要结果相对比，验证了基于二型模糊集合的摘要算法比基于一型模糊集合的摘要算法获得的摘要结果更准确。
4．针对数字音频信号分类问题，提出了基于二型模糊集合理论的C均值聚类算法，在此基础上应用跳跃基因遗传算法对聚类得到的初始模糊模型进行优化，并采用向量相似性测度准则对优化后的模糊规则进行简化，得到最终的模糊分类器模型。通过实例仿真验证了采用二型模糊C均值聚类算法的音频信号分类器比采用一型模糊C均值聚类算法的分类器得到的分类结果更准确。
In1975, Professor Zadeh introduced the type-2fuzzy set to expand the traditionalfuzzy set (type-1fuzzy set). Compared with the type-1fuzzy set, the most advantage ofthe type-2fuzzy set is to use three-dimensional membership functions, so themembership degree of the element becomes a fuzzy number in [01]. The type-2fuzzysets theory is more applicable to the case of the multi-uncertainty. For example, theshape or parameters of membership functions are uncertain. When an element can notbe entirely part of a set (the membership of the element that belongs to a set is between0and1), we choose type-1fuzzy sets. When the “membership” of an element belongsto a set is uncertain (the membership degree of an element is uncertain), we can choosethe type-2fuzzy sets.
Now, fuzzy systems have become most successful in the applications of fuzzy setstheory. The core of a fuzzy system is the knowledge database formed by fuzzyIF-THEN rules, which is based on the fuzzy sets theory. The same as the type-1fuzzysystem, type-2fuzzy system is also constructed by the combination of fuzzy IF-THENrules. However, the type-2fuzzy system is based on type-2fuzzy sets. Compared withthe type-1fuzzy system, the type-2fuzzy system can handle the multi-uncertaintydirectly. Because the high computational complexity of the type-2fuzzy sets, most ofthe applications of type-2fuzzy sets theory focus on the interval type-2fuzzy sets.Through constructing the model of interval type-2fuzzy systems, this PhD thesisstudies the applications of type-2fuzzy system in the field of automatic control andpattern recognition. The main contents are as follows:
1. Based on the discrete interval type-2T-S fuzzy model, a fuzzy control system isdesigned by using the parallel distributed compensation. By using the Lyapunovmethod, the stabilization of the control system is analyzed, and the results arepresented in terms of linear matrix inequalities. The effectiveness of the resultsis illustrated by numerical simulations. Finally, the type-2fuzzy controllerdesign for autonomous parallel parking on bumpy road is applied to detail the design processes. The simulation results have showed the advantages of type-2fuzzy control systems on handling the information of multi-uncertainty.
2. Based on the continued interval type-2T-S fuzzy model with time-varing delay,a fuzzy control system is designed by using the parallel distributedcompensation. By using the Lyapunov method, the stabilization of the controlsystem is analyzed, the results are presented in terms of linear matrixinequalities. The effectiveness of the control system is verified by numericalsimulations. Finally, the type-2fuzzy controller design for the continuous stirredtank reactor is discussed to detail the design process. The results of thesimulations further illustrate the advantages of type-2fuzzy control systems onhandling the information of multi-uncertainty.
3. Chinese automatic summarization method is discussed based on the type-2fuzzy C-means clustering. In order to use the cluster analysis method, it isnecessary to represent Chinese sentences as vectors of features. These featuresinclude sentence centrality, title feature, high frequency words feature, firstsentence feature, sentence length, sentence position, and numerical data feature.Based on the text clustering analysis, the key sentences of the text can be foundout to construct the summary. Finally, the experiments have showed that theChinese automatic summarization result is more accurate to adopt the type-2fuzzy system than to adopt the type-1fuzzy system.
4. Type-2fuzzy c-means clustering algorithm is applied to solve the digital audiosignal classification problem, and jumping genes genetic algorithm is used tooptimize the initial fuzzy model which is obtained by the clustering algorithm.At last, the optimized fuzzy rule base is simplified by the vector similaritymeasure, and the final fuzzy classifier model is obtained. Compared to theconventional type-1fuzzy sets, type-2fuzzy sets can handle more uncertaininformation. Especially for sample sets whose samples distribute uneven andstructures are irregular. The experiment results are more precise when the type-2fuzzy c-means clustering algorithm is adopted.
引文
[1] L.A. Zadeh. Fuzzy sets. Information and Control,1965,8:338-353
[2]王立新,王迎军.模糊系统与模糊控制教程.北京:清华大学出版社,2003
[3]汪培庄.模糊集合论及其应用.上海:上海科学技术出版社,1983
[4]王士同.模糊推理理论与模糊专家系统.上海:科学技术文献出版社,1995
[5]刘宝碇,彭锦.不确定理论教程.北京:清华大学出版社,2005
[6] R. E. Bellman, L. A. Zadeh. Decision-making in a fuzzy environment. Management Science,1970,17(4):141-164
[7] L.A. Zadeh. Similarity relations and fuzzy ordering. Information Sciences,1971,3(2):176-200
[8] D. Dubois, H. Prade. Fuzzy Sets and Systems: Theory and Applications, New York: AcademicPress,1980
[9] E. H. Mamdani, S. Assilian. An experiment in linguistic synthesis with a fuzzy logic controller.International Journal of Machine Studies,1975,7(1):1-13
[10]李洪兴,苗志宏,王加银.四级倒立摆的变论域自适应模糊控制.中国科学,2002,32(1):64-75
[11] J. M. Mendel, R. I. John. Type-2fuzzy sets made simple. IEEE Transactions of Fuzzy Systems,2002,10(2):116-127
[12] L. A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning-1.Information Sciences,1975,8:199-249
[13] J. M. Mendel. Type-2fuzzy sets and systems: an overview. IEEE Computational IntelligenceMagazine,2007,2(2):20-29
[14] M. Mizumoto, K. Tanaka. Some properties of fuzzy sets of type-2. Information and Control,1976,31:312-340
[15] M. Mizumoto, K. Tanaka. Fuzzy sets of type-2under algebraic product and algebraic sum.Fuzzy Sets and systems,1981,5:276-290
[16] J. Nieminen. On the algebraic structure of fuzzy sets of type-2. Kyveretica,1977,13(4):261-273
[17] A. Kaufmann, M. M. Gupta. Introduction to Fuzzy Arithmetic: Theory and Application, NewYork: Van Nostrand Reinhold,1991
[18] N. N. Karnik, J. M. Mendel. An introduction to type-2fuzzy logic systems. USC Report,October1998, http://sipi.usc.edu/~mendel/report
[19] N. N. Karnik, J. M. Mendel, Q. Liang. Type-2fuzzy logic systems. IEEE Transactions onFuzzy Systems,1999,7(6):642-658
[20] N. N. Karnik, J. M. Mendel. Operations on type-2fuzzy sets. Fuzzy Sets and Systems,2001,122:326-348
[21] E. Hisdal. The if then else statement and interval-valued fuzzy sets of higher type. InternationalJournal of Machine Studies,1981,15:384-455
[22] D. G. Schwartz. The case for an interval-based representation of linguistic truth. Fuzzy Sets andSystems,1985,17:152-165
[23] I. Turksen. Interval valued fuzzy sets based on normal forms. Fuzzy sets and systems,1986,20:191-210
[24] Q. Liang, J. M. Mendel. Interval type-2fuzzy logic systems: theory and design. IEEETransactions on Fuzzy systems,2000,8(5):534-550
[25] J. M. Mendel, R. I. John, F. Liu. Interval type-2fuzzy logic systems made simple. IEEETransactions on Fuzzy systems,2006,14(6):808-821
[26] J. M. Mendel. On the importance of interval sets in type-2fuzzy logic systems. Proceedings ofJoint9thIFSA Word Congress and20thNAFIPS International Conference, Vancouver, BC,Canada, July24-28,2001:1646-1652
[27] W. Wei, J. M. Mendel. A fuzzy logic method for modulation classification in nonidealenvironments. IEEE Transactions on Fuzzy Systems,1999,7:332-344
[28] G. J. Klir, B. Yuan. Fuzzy Sets and Fuzzy Logic: Theory and Application. Englewood Cliffs,New Jersey: Prentice-Hall,1995
[29] D. Driankov, H. Hellendoorn, M. Reinfrank. An Introduction to Fuzzy Control,2nd, New York:Springer-Verlag,1996
[30] N. N. Karnik, J. M. Mendel. Centroid of a type-2fuzzy sets. Information and Sciences,2001,132(1-4):194-220
[31] J. M. Mendel. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New directions,Upper Saddle River, New Jersey: Prentice-Hall,2001
[32] E. H. Mamdani. Fuzzy control-a misconception of theory and application. IEEE Expert,1994,9(4):26-28
[33] T. Takagi, M. Sugeno. Fuzzy identification of systems and its applications to modeling andcontrol. IEEE Transactions on Systems, Man and Cybemetics,1985,15(1):115-132
[34] K. Tanaka. A Theory of Advanced Fuzzy Control, Tokyo, Japan: KYOURITSU PublishingCompany,1994
[35] M. Sugeno, G. T. Kang. Structure identification of fuzzy model. Fuzzy Sets and Systems,1988,28(1):14-33
[36] M. Sugeno. Fuzzy Control, Tokyo: Nikkan Kougyou Shinbunsha Publisher,1988
[37] S. Kawamoto, K. Tada, A. Ishigame, T. Taniguchi. An approach to stability analysis of secondorder fuzzy systems. Proceedings of First IEEE International Conference on Fuzzy Systems,San Diego, CA, USA, March8-12,1992:1426-1434
[38] K. Tanaka, H. O. Wang. Fuzzy Control Systems Design and Analysis: A Linear MatrixInequality Approach, New York: John Wiley&Sons,2001
[39] M. Sugeno, G. T. Kang. Fuzzy modeling and control of multilayer incinerator. Fuzzy Sets andSystems,1986,18(1):329-346
[40] K. Tanaka, M. Sugeno. Stability analysis and design of fuzzy control systems. Fuzzy Sets andSystems,1992,45(2):134-156
[41] H. O. Wang, K. Tanaka, M. F. Griffin. Parallel distributed compensation of nonlinear systemsby Takagi-Sugeno fuzzy model. Proceedings of the4thIEEE International Conference onFuzzy Systems and the2ndInternational Fuzzy Engineering Symposium, Yokohama, Japan,March20-24,1995:531-538
[42] H. O. Wang, K. Tanaka, M. F. Griffin. An analytical framework of fuzzy modeling and controlof nonlinear systems: stability and design issues. Proceedings of1995American ControlConference, Seattle, WA, USA, June21-23,1995,3:2272-2276
[43] H. Hagras. Type-2FLCs: a new generation of fuzzy controllers. IEEE ComputationalIntelligence Magazine,2007,2(1):30-43
[44] C. Lynch, H. Hagras, V. Callaghan. Embedded type-2FLC for real-time speed control ofmarine and traction diesel engines. Proceedings of the14thIEEE International Conference onFuzzy Systems, Reno, Nevada, USA, May24-25,2005:346-352
[45] P. Lin, C. Hsu, T. Lee. Type-2fuzzy logic controller design for buck DC-DC converters.Proceedings of2005IEEE International Conference on Fuzzy Systems, Reno, Nevada, USA,May22-25,2005:364-370
[46] D. Wu, W. Tan. Type-2fuzzy logic controller for the liquid-level process. Proceedings of2004IEEE International Conference on Fuzzy Systems, Budapest, Hungary, July24-29,2004:248-253
[47] H. Hagras.Ahierarchical type-2fuzzy logic control architecture for autonomous mobile robots.IEEE Transactions on Fuzzy Systems,2004,12(1):523-539
[48] M. B. Begian, W. W. Melek, J. M. Mendel. Stability analysis of type-2fuzzy systems.Proceedings of IEEE World Congress on Computational Intelligence, Hong Kong, China, June1-6,2008:946-953
[49] H. K. Lam, L. D. Seneviratne. Stability analysis of interval type-2fuzzy-model-based controlsystems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics,2008,38(3):616-628
[50] S. Boyd, L. El Ghaoui, E. Feron, V. Balakrishnan. Linear Matrix Inequalities in System andControl Theory, Philadelphia Press,1994
[51] Q. Liang, J. M. Mendel. Equalization of nonlinear time-varing channels using type-2fuzzyadaptive filters. IEEE Transactions on Fuzzy Systems,2000,8(5):551-563
[52] K. Tanaka, T. Ikeda, H. O. Wang. Fuzzy regulators and fuzzy observers: relaxed stabilityconditions and LMI-based designs. IEEE Transactions on Fuzzy Systems,1998,6(2):250-265
[53] E. Kim, H. Lee. New approaches to relaxed quadratic stability condition of fuzzy controlsystems. IEEE Transactions on Fuzzy Systems,2000,8(5):522-534
[54] K. Tanaka, M. Sano. Trajectory stabilization of a model car via fuzzy control. Fuzzy Sets andSystems,1995,70(2-3):154-170
[55] J. P. Richard. Time-delay systems: an overview of some recent advances and open problems.Automatica,2003,39(10):1666-1694
[56] B. Chen, X. P. Liu, S. C. Tong. New delay-dependent stabilization conditions of T-S fuzzysystems with constant delay. Fuzzy Sets and Systems,2007,158(20):2209-2224
[57] C. L. Chen, G.. Feng, X. P. Guan. Delay-dependent stability analysis and controller synthesisfor discrete-time T-S fuzzy systems with time delays. IEEE Transactions on Fuzzy Systems,2005,13(5):630-643
[58] X. P. Guan, C. L. Chen. Delay-dependent guaranteed cost control for T-S fuzzy systems withtime delays. IEEE Transactions on Fuzzy Systems,2004,12(2):235-249
[59] C. G. Li, H. J. Wang, X. F. Liao. Delay-dependent robust stability of uncertain fuzzy systemswith time varying delays. IEE Proceedings-Control Theory and Applications,2004,151(4):416-421
[60] C. Lin, Q. G. Wang, T. H. Lee. Delay-dependent LMI conditions for stability and stabilizationof T-S fuzzy systems with bounded time-delay. Fuzzy Sets and Systems,2006,157(9):1229-1247
[61] C. Peng, Y. C. Tian, E. Tian. Improved delay-dependent robust stabilization conditions ofuncertain TCS fuzzy systems with time-varying delay. Fuzzy Sets and Systems,2008,159(20):2712-2729
[62] E. Tian, C. Peng. Delay-dependent stability analysis and synthesis of uncertain T-S fuzzysystems with time-varying delay. Fuzzy Sets and Systems,2006,157(4):543-559
[63] E. G. Tian, D. Yue, Y. J. Zhang. Delay-dependent robust H∞control for T-S fuzzy system withinterval time-varying delay. Fuzzy Sets and Systems,2009,160(12):1708-1719
[64] H. N. Wu, H. X. Li. New approach to delay-dependent stability analysis and stabilization forcontinuous-time fuzzy systems with time-varying delay. IEEE Transactions on Fuzzy Systems2007,15(3):482-493
[65] H. B. Zhang, C. G. Li, X. F. Liao. Stability analysis and H∞controller design of fuzzylarge-scale systems based on piecewise Lyapunov functions. IEEE Transactions on System,Man and Cybemetics, Part B: Cybernetics,2005,36(3):684-698
[66] X. W. Liu, H. B. Zhang. Delay-dependent robust stability of uncertain fuzzy large-scalesystems with time-varying delays. Automatica,2008,44(1):192-198
[67] Y. He, Q. G. Wang, L. H. Xie, C. Lin. Further improvement of free-weighting matricestechnique for systems with time-varying delay. IEEE Transactions on Automatic Control,2007,52(2):292-299
[68] M. B. Begian, W. W. Melek, J. M. Mendel. On the stability of interval type-2TSK fuzzy logiccontrol systems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics,2010,40(3):798-818
[69] Y. He, M. Wu, J. H. She, G.. P. Liu. Parameter-dependent Lyapunov functional for stability oftime-delay systems with polytopictype uncertainties. IEEE Transactions on Automatic Control,2004,49(5):828-832
[70] M. Wu, Y. He, J. H. She, G.. P. Liu. Delay-dependent criteria for robust stability oftime-varying delay systems. Automatic,2004,40(8):1434-1439
[71] B. Lehman, E. I. Verriest. Stability of a continuous stirred reactor with delay in the recyclestreams. Proceedings of30thIEEE Conference on Decision and Control, Brighton, UK,December11-13,1991:1874-1876
[72] Y. Y. Cao, P. M. Frank. Analysis and synthesis of nonlinear time-delay systems via fuzzycontrol approach. IEEE Transactions on Fuzzy Systems,2000,8(2):200-211
[73] C. H. Lien. Stabilization for uncertain Takagi-Sugeno fuzzy systems with time-varying delaysand bounded uncertainties. Chaos, Solitons and Fractals,2007,32:644-652
[74] R. J. Wang, W. W. Lin, W. J. Wang. Stabilizability of linear quadratic state feedback foruncertain fuzzy time-delay systems. IEEE Transactions on Systems, Man, andCybernetics-Part B: Cybernetics,2004,34(2):1288-1292
[75] B. Lehman, S. Verduyn. Vibrational control of nonlinear time lag systems with bounded delay:averaging theory, stabilizability, and transient behavior. IEEE Transactions on AutomaticControl,1994,39(5):898-912
[76] S. Theodoridis, K. Koutroumbas. Pattern Recognition4thEdition. Academic Press,2008
[77]李大健,齐敏,郝重阳.模式识别导论.北京:清华大学出版社,2009
[78]孙即祥.现代模式识别（第二版）.北京:高等教育出版社,2008
[79]孙亮.模式识别原理.北京:北京工业大学出版社,2009
[80] R. E. Bellman, R. Kalaba, L. A. Zadeh. Abstraction and pattern classification. Journal ofMathematical Analysis and applications,1966(13):1-7
[81] H. J. Xing, B. G. Hu. An adaptive fuzzy c-means clustering-based mixtures of experts modelfor uIIlabeled data classification. Neurocomputing,2008,71:1008-1021
[82] U. Maulik, I. Saha. Automatic fuzzy clustering using modified differential evolution for imageclassification. IEEE Transactions on Geoscience and Remote Sensing,2010,48(9):3502-3509
[83] C-C Hung, S. Kulkarni, B-C. Kuo. A new weighted fuzzy c-means clustering algorithm forremotely sensed image classification. IEEE Journal of Selected Topics in Signal Processing,2011,5(3):542-553
[84] K. Sikka, N. Sinha, P. K. Singh. A. K. Mishra. A fully automated algorithm under modifiedFCM framework for improved brain MR image segmentation. Magnetic Resonance Imaging,2009,27:993-1004
[85]张东波，王耀南. FCM聚类算法和粗糙集在医疗图像分割中的应用.仪器仪表学报,2006,27(12):1682-1687
[86] Q. Liang, J. M. Mendel. MPEG VBR video traffic modeling and classification using fuzzytechniques. IEEE Transactions on Fuzzy Systems,2001,9(1):182-193
[87] Q. Liang, J. M. Mendel. Overcoming time-varing co-channel interference using type-2fuzzyadaptive filter. IEEE Transactions on Circuits Systems II: Analog Digital Signal Process,2000,47:1419-1428
[88] Q. Liang, N. N. Karnik, J. M. Mendel. Connection admission control in ATM networks usingsurvey-based type-2fuzzy logic systems. IEEE Transactions on Systems, Man, andCybernetics, Part C: Applications and Reviews,2000,30:329-339
[89] K. Wu. Fuzzy interval control of mobile robots. Computers and Electrical Engineering,1996,22(3):211-229
[90] R. Yager. Fuzzy subsets of type II in decision. Journal of Cybernetics,1980,10:136-159
[91] N. N. Karnik, J. M. Mendel. Application of type-2fuzzy logic systems to forecasting of timeseries. Information Sciences,1999,120:89-111
[92] P. R. Innocent, R. I. John. Type-2fuzzy diagnosis. Proceedings of IEEE InternationalConference on Fuzzy Systems, Honolulu, HI, USA, May12-17,2002:1325-1330
[93] R. Krishnapuram, O. Nasraoui, H. Frigui. The fuzzy C-spherical shells algorithm. IEEETransactions on Neural Networks,1992,3(5):662-671
[94] J. C. Dunn. Well-separated clusters and the optimal fuzzy partitions. Journal of Cybernetics,1974,4(1):94-104
[95] J. Tou, R. Gonzalez. Pattern Recognition Principles. Reading, Massachusetts: Addison-Wesley,1974
[96] J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. New York:Plenum Press,1981
[97] J. M. Mendel. Computing derivatives in interval type-2fuzzy logic systems. IEEE Transactionson Fuzzy Systems,2004,12(1):83-98
[98] I. Ozkan, I. Turksen. Entropy assessment for type-2fuzziness. Proceedings of2004International Conference on Fuzzy Systems, Toronto, Canada, July24-29,2004:1111-1115
[99] F. C.-H. Rhee, C. Hwang. A type-2fuzzy C-means clustering algorithm. Proceedings of Joint9thIFSA Word Congress and20thNAFIPS International Conference, Vancouver, BC, Canada,July24-28,2001:1925-1929
[100] F. C.-H. Rhee, C. Hwang. An interval type-2fuzzy perceptron. Proceedings of the2002IEEEInternational Conference on Fuzzy Systems, Honolulu, HI, USA, May12-17,2002:132-1335
[101] F. C.-H. Rhee, C. Hwang. An interval type-2fuzzy K-nearest neighbor. Proceedings of the12thIEEE International Conference on Fuzzy Systems, Hanyang University, Ansan, SouthKorea, May24-28,2003:802-807
[102] C. Hwang, F. C.-H. Rhee. An interval type-2fuzzy C spherical shells algorithm. Proceedingsof the2004IEEE International Conference on Fuzzy Systems, Hanyang University, Ansan,South Korea, July24-29,2004:1116-1122
[103] C. Hwang, F. C.-H. Rhee. Uncertain fuzzy clustering: interval type-2fuzzy approach toc-means. IEEE Transactions on Fuzzy Systems,2007,15(1):106-119
[104]胡侠,林晔,王灿,林立.自动文本摘要技术综述.情报杂志,2010,29(8):143-147
[105]刘挺,王开铸.自动摘要的4种主要方法.情报学报,1999,18(1):10-19
[106]王志琪,王永成,刘传汉.论自动文摘及其分类.情报学报,2005,24(2):213-221
[107]孙春奎,自动文摘及其知识获取技术研究.北京:邮电大学,2000
[108] H. P. Luhn. The automatic creation of literature abstracts. IBM Journal of Research andDevelopment,1958,2(2):159-165
[109] K. Zechner. Fast generation of abstracts from general domain text corpora by extractingrelevant sentences. Proceedings of the16thConference on Computational Linguistics,Stroudsburg, PA, USA,1996,2:985-989
[110] J. Carbonell, J. Goldstein. The use of MMR, diversity-based reranking for reorderingdocuments and producing summaries. Proceedings of the21stAnnual International ACMSIGIR Conference on Research and Development in Information Retrieval, New York, NY,USA,1998:334-336
[111] T. Strzalkowski, J. Wang, B. Wise. Arobust practical text summarization system. Proceedingsof AAAI Intelligent Text Summarization Workshop, Stanford, CA, USA,1998:25-30
[112] A. Berger, V. O. Mittal. Query-relevant summarization using FAQs. Proceedings of the38thAnnual Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA,2000:293-301
[113] T. Nomoto, Y. Matsumoto. A new approach to unsupervised text summarization. Proceedingsof the24thAnnual International ACM SIGIR Conference on Research and Development inInformation Retrieval, New York, NY, USA,2001:25-34
[114] J. L. Klavans, J. Shaw. Lexical semantics in summarization. Proceedings of the1stAnnualWorkshop of the IFIP Working Group for NLP and KR, Nantes, France,2005
[115] D. R. Radev, K. R. McKeown. Generating natural language summaries from multiple on-linesources. Journal of Computational Linguistics–Special Issue on Natural LanguageGeneration,1998,24(3):470-500
[116] Y. Nakao, N. K. Kawasaki. An algorithm for one-page summarization of a long text based onthematic hierarchy detection. Proceedings of the38thAnnual Meeting on Association forComputational Linguistics, Stroudsburg, PA, USA,2000:302-309
[117] J. Goldstein, M. Kantrowitz, V. Mittal, J. G. Carbonell. Summarizing text documents:sentence selection and evaluation metrics. Proceedings of the22ndAnnual International ACMSIGIR Conference on Research and Development in Information Retrieval, New York, NY,USA, August14-19,1999:121-128
[118] I. Mani.Automatic Summarization. Washington: John Benjamins Publishing Co.2001
[119] R. Barzilay, M. Elbadad. Using lexical chains for text summarization. Proceedings of theAOL Workshop on Intelligent Scalable Text Summarization, Madrid, Spain,1997:10-17
[120]万敏,罗振声,季姮,高小云.基于概念统计的英文自动摘要研究.计算机工程与应用,2002,38(24):6-16
[121]孙建军,成颖.信息检索技术.北京:科学出版社,2004
[122]王建波,王开铸.自然语言篇章理解及基于理解的自动文摘研究.中文信息学报,2001,6(2):24-27
[123]黄崑,符绍宏.自动分词技术及其在信息检索中应用的研究.现代图书情报技术,2001(1):25-29
[124]李家福,张亚非.基于EM算法的汉语自动分词算法.情报学报,2002(6):269-272
[125] C. Y. Lin. Rouge: a package for automatic evaluation of summaries. Proceedings of theACL-04Workshop: Text Summarization Branches Out, Barcelona, Spain,2004:73-81
[126] http://www.chinadaily.com.cn/zgrbjx/index.html
[127] S. G. Mallat.AWavelet Tour of Signal Processing. New York:Academic,1999
[128] J. Junqua, J. Haton. Robustness inAutomatic Speech Recognition. Boston: KluwerAcademic,1996
[129] B. Gold, N. Morgan. Speech and Audio Signal Processing: Processing and Perception ofSpeech and Music. Wiley,2000
[130] J. Ajmera, I. McCowan, H. Bourlard. Speech/music segmentation using entropy anddynamism features in a HMM classification framework. Speech Communication,2003,40:351-363
[131] L. Lu, H. J. Zhang, S. Z. Li. Content-based audio classification and segmentation by usingsupport vector machines. Multimedia Systems,2003,8(6):482-492
[132] P. Dhanalakshmi, S. Palanivel, V. Ramalingam. Classification of audio signals using SVMand RBFNN. Expert Systems with Applications,2009,36:6069-6075
[133] T. McConaghy, H. Leung, E. Boss, V. Varadan. Classification of audio rada signals usingradial basis function neural networks. IEEE Transactions on Instrumentation andMeasurement Society,2003,52(6):1771-1779
[134] S. Z. Li. Content-based audio classification and retrieval using the nearest feature line method.IEEE Transactions on Speech Audio Processing,2000,8(5):619-625
[135] B. McClintock. The origin and behavior of mutable loci in maize. Proceedings of the NationalAcademy Sciences, USA, Jun.,1950,36(6):343-355
[136] B. McClintock. Chromosome organization and genic expression. Cold Spring HarborSymposia on Quantitative Biology,1951,16:12-47
[137] N. Fedoroff, D. Botstein. The Dynamic Genome: Barbara McClintock’s Ideas in the Centuryof Genetics. New York: Cold Spring Harbor,1992
[138] L. H. Caporale, J. Genes. Darvin in the Genome: Molecular Strategies in Biological Evolution.New York: McGraw-Hill,2003
[139] L. Margulis, D. Sagan. Acquiring Genomes: A Theory of the Origins of Species. New York:Basic Books,2002
[140] K. F. Man, T. M. Chan, K. S. Tang, S. Kwong. Jumping-Genes in evolutionary computing.Proceedings of the IEEE Industrial Electronic Society, Busan, Korea, Nov.2-8,2004:1268-1272
[141] K. S. Tang, S. Kwong, K. F. Man. A jumping genes paradigm: theory, verification, andapplication. IEEE Circuits and Systems Magazine,2008,8(4):18-38
[142] T. M. Chan, K. F. Man, K. S. Tang, S. Kwong. A jumping gene paradigm for evolutionarymultiobjective optimization. IEEE Transactions on Evolutionary Computation,2008,12(2):142-159
[143] T. M. Chan, K. F. Man, K. S. Tang, S. Kwong. A jumping gene algorithm for multiobjectiveresource management in wideband CDMA systems. The Computer Journal,2005,48(6):749-768
[144] T. M. Chan, K. F. Man, K. S. Tang, S. Kwong. Multiobjective optimization of radio-to-fiberrepeater placement using a jumping gene algorithm. Proceedings of the IEEE InternationalConference on Industrial Technology, Hong Kong, China, Dec.13-17,2005:291-296
[145] T. M. Chan, K. F. Man, K. S. Tang, S. Kwong. A jumping-genes paradigm for optimizingfactory WLAN network. IEEE Transactions on Industrial Informatics,2007,3(1):32-43
[146] D. Wu, J. M. Mendel. A vector similarity measure for linguistic approximation: intervaltype-2and type-1fuzzy sets. Information Sciences,2008,178:381-402.
[147] L. Lu, H. J. Zhang, H. Jiang. Content analysis for audio classification and segmentation. IEEETransactions on Speech and Audio Processing,2002,10(7):503-513
[148] L. Sheng, X. Y. Ma. JGGA-Fuzzy classification of audio signals. Proceedings of4thIEEEInternational Conference on Computer Science and Information Technology, Chengdu, China,Jun.10-12,2011,5:371-375
[2]王立新,王迎军.模糊系统与模糊控制教程.北京:清华大学出版社,2003
[3]汪培庄.模糊集合论及其应用.上海:上海科学技术出版社,1983
[4]王士同.模糊推理理论与模糊专家系统.上海:科学技术文献出版社,1995
[5]刘宝碇,彭锦.不确定理论教程.北京:清华大学出版社,2005
[6] R. E. Bellman, L. A. Zadeh. Decision-making in a fuzzy environment. Management Science,1970,17(4):141-164
[7] L.A. Zadeh. Similarity relations and fuzzy ordering. Information Sciences,1971,3(2):176-200
[8] D. Dubois, H. Prade. Fuzzy Sets and Systems: Theory and Applications, New York: AcademicPress,1980
[9] E. H. Mamdani, S. Assilian. An experiment in linguistic synthesis with a fuzzy logic controller.International Journal of Machine Studies,1975,7(1):1-13
[10]李洪兴,苗志宏,王加银.四级倒立摆的变论域自适应模糊控制.中国科学,2002,32(1):64-75
[11] J. M. Mendel, R. I. John. Type-2fuzzy sets made simple. IEEE Transactions of Fuzzy Systems,2002,10(2):116-127
[12] L. A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning-1.Information Sciences,1975,8:199-249
[13] J. M. Mendel. Type-2fuzzy sets and systems: an overview. IEEE Computational IntelligenceMagazine,2007,2(2):20-29
[14] M. Mizumoto, K. Tanaka. Some properties of fuzzy sets of type-2. Information and Control,1976,31:312-340
[15] M. Mizumoto, K. Tanaka. Fuzzy sets of type-2under algebraic product and algebraic sum.Fuzzy Sets and systems,1981,5:276-290
[16] J. Nieminen. On the algebraic structure of fuzzy sets of type-2. Kyveretica,1977,13(4):261-273
[17] A. Kaufmann, M. M. Gupta. Introduction to Fuzzy Arithmetic: Theory and Application, NewYork: Van Nostrand Reinhold,1991
[18] N. N. Karnik, J. M. Mendel. An introduction to type-2fuzzy logic systems. USC Report,October1998, http://sipi.usc.edu/~mendel/report
[19] N. N. Karnik, J. M. Mendel, Q. Liang. Type-2fuzzy logic systems. IEEE Transactions onFuzzy Systems,1999,7(6):642-658
[20] N. N. Karnik, J. M. Mendel. Operations on type-2fuzzy sets. Fuzzy Sets and Systems,2001,122:326-348
[21] E. Hisdal. The if then else statement and interval-valued fuzzy sets of higher type. InternationalJournal of Machine Studies,1981,15:384-455
[22] D. G. Schwartz. The case for an interval-based representation of linguistic truth. Fuzzy Sets andSystems,1985,17:152-165
[23] I. Turksen. Interval valued fuzzy sets based on normal forms. Fuzzy sets and systems,1986,20:191-210
[24] Q. Liang, J. M. Mendel. Interval type-2fuzzy logic systems: theory and design. IEEETransactions on Fuzzy systems,2000,8(5):534-550
[25] J. M. Mendel, R. I. John, F. Liu. Interval type-2fuzzy logic systems made simple. IEEETransactions on Fuzzy systems,2006,14(6):808-821
[26] J. M. Mendel. On the importance of interval sets in type-2fuzzy logic systems. Proceedings ofJoint9thIFSA Word Congress and20thNAFIPS International Conference, Vancouver, BC,Canada, July24-28,2001:1646-1652
[27] W. Wei, J. M. Mendel. A fuzzy logic method for modulation classification in nonidealenvironments. IEEE Transactions on Fuzzy Systems,1999,7:332-344
[28] G. J. Klir, B. Yuan. Fuzzy Sets and Fuzzy Logic: Theory and Application. Englewood Cliffs,New Jersey: Prentice-Hall,1995
[29] D. Driankov, H. Hellendoorn, M. Reinfrank. An Introduction to Fuzzy Control,2nd, New York:Springer-Verlag,1996
[30] N. N. Karnik, J. M. Mendel. Centroid of a type-2fuzzy sets. Information and Sciences,2001,132(1-4):194-220
[31] J. M. Mendel. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New directions,Upper Saddle River, New Jersey: Prentice-Hall,2001
[32] E. H. Mamdani. Fuzzy control-a misconception of theory and application. IEEE Expert,1994,9(4):26-28
[33] T. Takagi, M. Sugeno. Fuzzy identification of systems and its applications to modeling andcontrol. IEEE Transactions on Systems, Man and Cybemetics,1985,15(1):115-132
[34] K. Tanaka. A Theory of Advanced Fuzzy Control, Tokyo, Japan: KYOURITSU PublishingCompany,1994
[35] M. Sugeno, G. T. Kang. Structure identification of fuzzy model. Fuzzy Sets and Systems,1988,28(1):14-33
[36] M. Sugeno. Fuzzy Control, Tokyo: Nikkan Kougyou Shinbunsha Publisher,1988
[37] S. Kawamoto, K. Tada, A. Ishigame, T. Taniguchi. An approach to stability analysis of secondorder fuzzy systems. Proceedings of First IEEE International Conference on Fuzzy Systems,San Diego, CA, USA, March8-12,1992:1426-1434
[38] K. Tanaka, H. O. Wang. Fuzzy Control Systems Design and Analysis: A Linear MatrixInequality Approach, New York: John Wiley&Sons,2001
[39] M. Sugeno, G. T. Kang. Fuzzy modeling and control of multilayer incinerator. Fuzzy Sets andSystems,1986,18(1):329-346
[40] K. Tanaka, M. Sugeno. Stability analysis and design of fuzzy control systems. Fuzzy Sets andSystems,1992,45(2):134-156
[41] H. O. Wang, K. Tanaka, M. F. Griffin. Parallel distributed compensation of nonlinear systemsby Takagi-Sugeno fuzzy model. Proceedings of the4thIEEE International Conference onFuzzy Systems and the2ndInternational Fuzzy Engineering Symposium, Yokohama, Japan,March20-24,1995:531-538
[42] H. O. Wang, K. Tanaka, M. F. Griffin. An analytical framework of fuzzy modeling and controlof nonlinear systems: stability and design issues. Proceedings of1995American ControlConference, Seattle, WA, USA, June21-23,1995,3:2272-2276
[43] H. Hagras. Type-2FLCs: a new generation of fuzzy controllers. IEEE ComputationalIntelligence Magazine,2007,2(1):30-43
[44] C. Lynch, H. Hagras, V. Callaghan. Embedded type-2FLC for real-time speed control ofmarine and traction diesel engines. Proceedings of the14thIEEE International Conference onFuzzy Systems, Reno, Nevada, USA, May24-25,2005:346-352
[45] P. Lin, C. Hsu, T. Lee. Type-2fuzzy logic controller design for buck DC-DC converters.Proceedings of2005IEEE International Conference on Fuzzy Systems, Reno, Nevada, USA,May22-25,2005:364-370
[46] D. Wu, W. Tan. Type-2fuzzy logic controller for the liquid-level process. Proceedings of2004IEEE International Conference on Fuzzy Systems, Budapest, Hungary, July24-29,2004:248-253
[47] H. Hagras.Ahierarchical type-2fuzzy logic control architecture for autonomous mobile robots.IEEE Transactions on Fuzzy Systems,2004,12(1):523-539
[48] M. B. Begian, W. W. Melek, J. M. Mendel. Stability analysis of type-2fuzzy systems.Proceedings of IEEE World Congress on Computational Intelligence, Hong Kong, China, June1-6,2008:946-953
[49] H. K. Lam, L. D. Seneviratne. Stability analysis of interval type-2fuzzy-model-based controlsystems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics,2008,38(3):616-628
[50] S. Boyd, L. El Ghaoui, E. Feron, V. Balakrishnan. Linear Matrix Inequalities in System andControl Theory, Philadelphia Press,1994
[51] Q. Liang, J. M. Mendel. Equalization of nonlinear time-varing channels using type-2fuzzyadaptive filters. IEEE Transactions on Fuzzy Systems,2000,8(5):551-563
[52] K. Tanaka, T. Ikeda, H. O. Wang. Fuzzy regulators and fuzzy observers: relaxed stabilityconditions and LMI-based designs. IEEE Transactions on Fuzzy Systems,1998,6(2):250-265
[53] E. Kim, H. Lee. New approaches to relaxed quadratic stability condition of fuzzy controlsystems. IEEE Transactions on Fuzzy Systems,2000,8(5):522-534
[54] K. Tanaka, M. Sano. Trajectory stabilization of a model car via fuzzy control. Fuzzy Sets andSystems,1995,70(2-3):154-170
[55] J. P. Richard. Time-delay systems: an overview of some recent advances and open problems.Automatica,2003,39(10):1666-1694
[56] B. Chen, X. P. Liu, S. C. Tong. New delay-dependent stabilization conditions of T-S fuzzysystems with constant delay. Fuzzy Sets and Systems,2007,158(20):2209-2224
[57] C. L. Chen, G.. Feng, X. P. Guan. Delay-dependent stability analysis and controller synthesisfor discrete-time T-S fuzzy systems with time delays. IEEE Transactions on Fuzzy Systems,2005,13(5):630-643
[58] X. P. Guan, C. L. Chen. Delay-dependent guaranteed cost control for T-S fuzzy systems withtime delays. IEEE Transactions on Fuzzy Systems,2004,12(2):235-249
[59] C. G. Li, H. J. Wang, X. F. Liao. Delay-dependent robust stability of uncertain fuzzy systemswith time varying delays. IEE Proceedings-Control Theory and Applications,2004,151(4):416-421
[60] C. Lin, Q. G. Wang, T. H. Lee. Delay-dependent LMI conditions for stability and stabilizationof T-S fuzzy systems with bounded time-delay. Fuzzy Sets and Systems,2006,157(9):1229-1247
[61] C. Peng, Y. C. Tian, E. Tian. Improved delay-dependent robust stabilization conditions ofuncertain TCS fuzzy systems with time-varying delay. Fuzzy Sets and Systems,2008,159(20):2712-2729
[62] E. Tian, C. Peng. Delay-dependent stability analysis and synthesis of uncertain T-S fuzzysystems with time-varying delay. Fuzzy Sets and Systems,2006,157(4):543-559
[63] E. G. Tian, D. Yue, Y. J. Zhang. Delay-dependent robust H∞control for T-S fuzzy system withinterval time-varying delay. Fuzzy Sets and Systems,2009,160(12):1708-1719
[64] H. N. Wu, H. X. Li. New approach to delay-dependent stability analysis and stabilization forcontinuous-time fuzzy systems with time-varying delay. IEEE Transactions on Fuzzy Systems2007,15(3):482-493
[65] H. B. Zhang, C. G. Li, X. F. Liao. Stability analysis and H∞controller design of fuzzylarge-scale systems based on piecewise Lyapunov functions. IEEE Transactions on System,Man and Cybemetics, Part B: Cybernetics,2005,36(3):684-698
[66] X. W. Liu, H. B. Zhang. Delay-dependent robust stability of uncertain fuzzy large-scalesystems with time-varying delays. Automatica,2008,44(1):192-198
[67] Y. He, Q. G. Wang, L. H. Xie, C. Lin. Further improvement of free-weighting matricestechnique for systems with time-varying delay. IEEE Transactions on Automatic Control,2007,52(2):292-299
[68] M. B. Begian, W. W. Melek, J. M. Mendel. On the stability of interval type-2TSK fuzzy logiccontrol systems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics,2010,40(3):798-818
[69] Y. He, M. Wu, J. H. She, G.. P. Liu. Parameter-dependent Lyapunov functional for stability oftime-delay systems with polytopictype uncertainties. IEEE Transactions on Automatic Control,2004,49(5):828-832
[70] M. Wu, Y. He, J. H. She, G.. P. Liu. Delay-dependent criteria for robust stability oftime-varying delay systems. Automatic,2004,40(8):1434-1439
[71] B. Lehman, E. I. Verriest. Stability of a continuous stirred reactor with delay in the recyclestreams. Proceedings of30thIEEE Conference on Decision and Control, Brighton, UK,December11-13,1991:1874-1876
[72] Y. Y. Cao, P. M. Frank. Analysis and synthesis of nonlinear time-delay systems via fuzzycontrol approach. IEEE Transactions on Fuzzy Systems,2000,8(2):200-211
[73] C. H. Lien. Stabilization for uncertain Takagi-Sugeno fuzzy systems with time-varying delaysand bounded uncertainties. Chaos, Solitons and Fractals,2007,32:644-652
[74] R. J. Wang, W. W. Lin, W. J. Wang. Stabilizability of linear quadratic state feedback foruncertain fuzzy time-delay systems. IEEE Transactions on Systems, Man, andCybernetics-Part B: Cybernetics,2004,34(2):1288-1292
[75] B. Lehman, S. Verduyn. Vibrational control of nonlinear time lag systems with bounded delay:averaging theory, stabilizability, and transient behavior. IEEE Transactions on AutomaticControl,1994,39(5):898-912
[76] S. Theodoridis, K. Koutroumbas. Pattern Recognition4thEdition. Academic Press,2008
[77]李大健,齐敏,郝重阳.模式识别导论.北京:清华大学出版社,2009
[78]孙即祥.现代模式识别（第二版）.北京:高等教育出版社,2008
[79]孙亮.模式识别原理.北京:北京工业大学出版社,2009
[80] R. E. Bellman, R. Kalaba, L. A. Zadeh. Abstraction and pattern classification. Journal ofMathematical Analysis and applications,1966(13):1-7
[81] H. J. Xing, B. G. Hu. An adaptive fuzzy c-means clustering-based mixtures of experts modelfor uIIlabeled data classification. Neurocomputing,2008,71:1008-1021
[82] U. Maulik, I. Saha. Automatic fuzzy clustering using modified differential evolution for imageclassification. IEEE Transactions on Geoscience and Remote Sensing,2010,48(9):3502-3509
[83] C-C Hung, S. Kulkarni, B-C. Kuo. A new weighted fuzzy c-means clustering algorithm forremotely sensed image classification. IEEE Journal of Selected Topics in Signal Processing,2011,5(3):542-553
[84] K. Sikka, N. Sinha, P. K. Singh. A. K. Mishra. A fully automated algorithm under modifiedFCM framework for improved brain MR image segmentation. Magnetic Resonance Imaging,2009,27:993-1004
[85]张东波，王耀南. FCM聚类算法和粗糙集在医疗图像分割中的应用.仪器仪表学报,2006,27(12):1682-1687
[86] Q. Liang, J. M. Mendel. MPEG VBR video traffic modeling and classification using fuzzytechniques. IEEE Transactions on Fuzzy Systems,2001,9(1):182-193
[87] Q. Liang, J. M. Mendel. Overcoming time-varing co-channel interference using type-2fuzzyadaptive filter. IEEE Transactions on Circuits Systems II: Analog Digital Signal Process,2000,47:1419-1428
[88] Q. Liang, N. N. Karnik, J. M. Mendel. Connection admission control in ATM networks usingsurvey-based type-2fuzzy logic systems. IEEE Transactions on Systems, Man, andCybernetics, Part C: Applications and Reviews,2000,30:329-339
[89] K. Wu. Fuzzy interval control of mobile robots. Computers and Electrical Engineering,1996,22(3):211-229
[90] R. Yager. Fuzzy subsets of type II in decision. Journal of Cybernetics,1980,10:136-159
[91] N. N. Karnik, J. M. Mendel. Application of type-2fuzzy logic systems to forecasting of timeseries. Information Sciences,1999,120:89-111
[92] P. R. Innocent, R. I. John. Type-2fuzzy diagnosis. Proceedings of IEEE InternationalConference on Fuzzy Systems, Honolulu, HI, USA, May12-17,2002:1325-1330
[93] R. Krishnapuram, O. Nasraoui, H. Frigui. The fuzzy C-spherical shells algorithm. IEEETransactions on Neural Networks,1992,3(5):662-671
[94] J. C. Dunn. Well-separated clusters and the optimal fuzzy partitions. Journal of Cybernetics,1974,4(1):94-104
[95] J. Tou, R. Gonzalez. Pattern Recognition Principles. Reading, Massachusetts: Addison-Wesley,1974
[96] J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. New York:Plenum Press,1981
[97] J. M. Mendel. Computing derivatives in interval type-2fuzzy logic systems. IEEE Transactionson Fuzzy Systems,2004,12(1):83-98
[98] I. Ozkan, I. Turksen. Entropy assessment for type-2fuzziness. Proceedings of2004International Conference on Fuzzy Systems, Toronto, Canada, July24-29,2004:1111-1115
[99] F. C.-H. Rhee, C. Hwang. A type-2fuzzy C-means clustering algorithm. Proceedings of Joint9thIFSA Word Congress and20thNAFIPS International Conference, Vancouver, BC, Canada,July24-28,2001:1925-1929
[100] F. C.-H. Rhee, C. Hwang. An interval type-2fuzzy perceptron. Proceedings of the2002IEEEInternational Conference on Fuzzy Systems, Honolulu, HI, USA, May12-17,2002:132-1335
[101] F. C.-H. Rhee, C. Hwang. An interval type-2fuzzy K-nearest neighbor. Proceedings of the12thIEEE International Conference on Fuzzy Systems, Hanyang University, Ansan, SouthKorea, May24-28,2003:802-807
[102] C. Hwang, F. C.-H. Rhee. An interval type-2fuzzy C spherical shells algorithm. Proceedingsof the2004IEEE International Conference on Fuzzy Systems, Hanyang University, Ansan,South Korea, July24-29,2004:1116-1122
[103] C. Hwang, F. C.-H. Rhee. Uncertain fuzzy clustering: interval type-2fuzzy approach toc-means. IEEE Transactions on Fuzzy Systems,2007,15(1):106-119
[104]胡侠,林晔,王灿,林立.自动文本摘要技术综述.情报杂志,2010,29(8):143-147
[105]刘挺,王开铸.自动摘要的4种主要方法.情报学报,1999,18(1):10-19
[106]王志琪,王永成,刘传汉.论自动文摘及其分类.情报学报,2005,24(2):213-221
[107]孙春奎,自动文摘及其知识获取技术研究.北京:邮电大学,2000
[108] H. P. Luhn. The automatic creation of literature abstracts. IBM Journal of Research andDevelopment,1958,2(2):159-165
[109] K. Zechner. Fast generation of abstracts from general domain text corpora by extractingrelevant sentences. Proceedings of the16thConference on Computational Linguistics,Stroudsburg, PA, USA,1996,2:985-989
[110] J. Carbonell, J. Goldstein. The use of MMR, diversity-based reranking for reorderingdocuments and producing summaries. Proceedings of the21stAnnual International ACMSIGIR Conference on Research and Development in Information Retrieval, New York, NY,USA,1998:334-336
[111] T. Strzalkowski, J. Wang, B. Wise. Arobust practical text summarization system. Proceedingsof AAAI Intelligent Text Summarization Workshop, Stanford, CA, USA,1998:25-30
[112] A. Berger, V. O. Mittal. Query-relevant summarization using FAQs. Proceedings of the38thAnnual Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA,2000:293-301
[113] T. Nomoto, Y. Matsumoto. A new approach to unsupervised text summarization. Proceedingsof the24thAnnual International ACM SIGIR Conference on Research and Development inInformation Retrieval, New York, NY, USA,2001:25-34
[114] J. L. Klavans, J. Shaw. Lexical semantics in summarization. Proceedings of the1stAnnualWorkshop of the IFIP Working Group for NLP and KR, Nantes, France,2005
[115] D. R. Radev, K. R. McKeown. Generating natural language summaries from multiple on-linesources. Journal of Computational Linguistics–Special Issue on Natural LanguageGeneration,1998,24(3):470-500
[116] Y. Nakao, N. K. Kawasaki. An algorithm for one-page summarization of a long text based onthematic hierarchy detection. Proceedings of the38thAnnual Meeting on Association forComputational Linguistics, Stroudsburg, PA, USA,2000:302-309
[117] J. Goldstein, M. Kantrowitz, V. Mittal, J. G. Carbonell. Summarizing text documents:sentence selection and evaluation metrics. Proceedings of the22ndAnnual International ACMSIGIR Conference on Research and Development in Information Retrieval, New York, NY,USA, August14-19,1999:121-128
[118] I. Mani.Automatic Summarization. Washington: John Benjamins Publishing Co.2001
[119] R. Barzilay, M. Elbadad. Using lexical chains for text summarization. Proceedings of theAOL Workshop on Intelligent Scalable Text Summarization, Madrid, Spain,1997:10-17
[120]万敏,罗振声,季姮,高小云.基于概念统计的英文自动摘要研究.计算机工程与应用,2002,38(24):6-16
[121]孙建军,成颖.信息检索技术.北京:科学出版社,2004
[122]王建波,王开铸.自然语言篇章理解及基于理解的自动文摘研究.中文信息学报,2001,6(2):24-27
[123]黄崑,符绍宏.自动分词技术及其在信息检索中应用的研究.现代图书情报技术,2001(1):25-29
[124]李家福,张亚非.基于EM算法的汉语自动分词算法.情报学报,2002(6):269-272
[125] C. Y. Lin. Rouge: a package for automatic evaluation of summaries. Proceedings of theACL-04Workshop: Text Summarization Branches Out, Barcelona, Spain,2004:73-81
[126] http://www.chinadaily.com.cn/zgrbjx/index.html
[127] S. G. Mallat.AWavelet Tour of Signal Processing. New York:Academic,1999
[128] J. Junqua, J. Haton. Robustness inAutomatic Speech Recognition. Boston: KluwerAcademic,1996
[129] B. Gold, N. Morgan. Speech and Audio Signal Processing: Processing and Perception ofSpeech and Music. Wiley,2000
[130] J. Ajmera, I. McCowan, H. Bourlard. Speech/music segmentation using entropy anddynamism features in a HMM classification framework. Speech Communication,2003,40:351-363
[131] L. Lu, H. J. Zhang, S. Z. Li. Content-based audio classification and segmentation by usingsupport vector machines. Multimedia Systems,2003,8(6):482-492
[132] P. Dhanalakshmi, S. Palanivel, V. Ramalingam. Classification of audio signals using SVMand RBFNN. Expert Systems with Applications,2009,36:6069-6075
[133] T. McConaghy, H. Leung, E. Boss, V. Varadan. Classification of audio rada signals usingradial basis function neural networks. IEEE Transactions on Instrumentation andMeasurement Society,2003,52(6):1771-1779
[134] S. Z. Li. Content-based audio classification and retrieval using the nearest feature line method.IEEE Transactions on Speech Audio Processing,2000,8(5):619-625
[135] B. McClintock. The origin and behavior of mutable loci in maize. Proceedings of the NationalAcademy Sciences, USA, Jun.,1950,36(6):343-355
[136] B. McClintock. Chromosome organization and genic expression. Cold Spring HarborSymposia on Quantitative Biology,1951,16:12-47
[137] N. Fedoroff, D. Botstein. The Dynamic Genome: Barbara McClintock’s Ideas in the Centuryof Genetics. New York: Cold Spring Harbor,1992
[138] L. H. Caporale, J. Genes. Darvin in the Genome: Molecular Strategies in Biological Evolution.New York: McGraw-Hill,2003
[139] L. Margulis, D. Sagan. Acquiring Genomes: A Theory of the Origins of Species. New York:Basic Books,2002
[140] K. F. Man, T. M. Chan, K. S. Tang, S. Kwong. Jumping-Genes in evolutionary computing.Proceedings of the IEEE Industrial Electronic Society, Busan, Korea, Nov.2-8,2004:1268-1272
[141] K. S. Tang, S. Kwong, K. F. Man. A jumping genes paradigm: theory, verification, andapplication. IEEE Circuits and Systems Magazine,2008,8(4):18-38
[142] T. M. Chan, K. F. Man, K. S. Tang, S. Kwong. A jumping gene paradigm for evolutionarymultiobjective optimization. IEEE Transactions on Evolutionary Computation,2008,12(2):142-159
[143] T. M. Chan, K. F. Man, K. S. Tang, S. Kwong. A jumping gene algorithm for multiobjectiveresource management in wideband CDMA systems. The Computer Journal,2005,48(6):749-768
[144] T. M. Chan, K. F. Man, K. S. Tang, S. Kwong. Multiobjective optimization of radio-to-fiberrepeater placement using a jumping gene algorithm. Proceedings of the IEEE InternationalConference on Industrial Technology, Hong Kong, China, Dec.13-17,2005:291-296
[145] T. M. Chan, K. F. Man, K. S. Tang, S. Kwong. A jumping-genes paradigm for optimizingfactory WLAN network. IEEE Transactions on Industrial Informatics,2007,3(1):32-43
[146] D. Wu, J. M. Mendel. A vector similarity measure for linguistic approximation: intervaltype-2and type-1fuzzy sets. Information Sciences,2008,178:381-402.
[147] L. Lu, H. J. Zhang, H. Jiang. Content analysis for audio classification and segmentation. IEEETransactions on Speech and Audio Processing,2002,10(7):503-513
[148] L. Sheng, X. Y. Ma. JGGA-Fuzzy classification of audio signals. Proceedings of4thIEEEInternational Conference on Computer Science and Information Technology, Chengdu, China,Jun.10-12,2011,5:371-375