Fuzzy Sets and Systems 160 (2009) 706–713www.elsevier.com/locate/fss

Recent Literature

Collected byDidier Dubois, Henri Prade and Salvatore Sessa

BOOKSSpecial issues (with abbreviated titles)

• MDAI: V. Torra, Y. Narukawa, T. Gakuen, Eds.,Special Issue on “Modeling Decisions for Artifi-cial Intelligence”, Internat. J. Intelligent Systems23(2) (2008).

• SCS: D. Filev, H. Ying, Eds., Special Issue on“Soft Computing Systems”, Internat. J. GeneralSystems 37(1) (2008).

Papers1. Basic concepts1.1. Surveys and introductory papers

• S. Gottwald, Foundations of a set theory for fuzzysets—40 years of development, SCS 69–82.

• M. Nikravesh, Evolution of fuzzy logic: from in-telligent systems and computation to human mind,Soft Comput. 12(2) (2008) 207–214.

1.2. Various kinds of fuzzy sets and related issues

• D. Dubois, H. Prade, Gradual elements in a fuzzyset, Soft Comput. 12(2) (2008) 165–176.

1.3. Fuzzy set-theoretic and aggregation operators

• I. Couso, S. Montes, An axiomatic definition offuzzy divergence measures, Internat. J. of Uncer-tainty Fuzziness and Knowledge-Based Systems16(1) (2008) 1–18.

• J. Dombi, Towards a general class of operatorsfor fuzzy systems, IEEE Trans. on Fuzzy Systems16(2) (2008) 477–484.

0165-0114/$ - see front matterdoi:10.1016/j.fss.2008.10.001

• B. Jayaram, On the law of importation (x∧ y)→z≡ (x→ (y→ z)) in fuzzy logic, IEEE Trans. onFuzzy Systems 16(1) (2008) 130–144.

• F. Liu, J.M. Mendel, Aggregation using thefuzzy weighted average as computed by theKarnik–Mendel algorithms, IEEE Trans. on FuzzySystems 16(1) (2008) 1–12.

1.4. Measures of information and comparison

• X.W. Liu, Q.L. Da, On the properties of regularincreasing monotone (RIM) quantifiers with maxi-mum entropy, Internat. J. of General Systems 37(2)(2008) 167–180.

1.5. Fuzzy relations1.5.1. Similarities and fuzzy partial orderings

• U. Bodenhofer, M. Demirci, Strict fuzzy orderingswith a given context of similarity, Internat. J. ofUncertainty Fuzziness and Knowledge-Based Sys-tems 16(2) (2008) 147–178.

• J. Recasens, D. Boixader, A map character-izing the fuzzy points and columns of a t-indistinguishability operator, Internat. J. of Uncer-tainty Fuzziness and Knowledge-Based Systems16(2) (2008) 129–146.

1.5.2. Equation solving

• B.S. Shieh, New resolution of finite fuzzy relationequations with max–min composition, Internat. J.

Recent Literature / Fuzzy Sets and Systems 160 (2009) 706–713 707

of Uncertainty Fuzziness and Knowledge-BasedSystems 16(1) (2008) 19–34.

• Y.K. Wu, S.M. Guu, An efficient procedure forsolving a fuzzy relational equation with max-Archimedean t-norm composition, IEEE Trans.on Fuzzy Systems 16(1) (2008) 73–84.

1.5.3. Algebraic aspects

• C.T. Pang, On the asymptotic period of powers ofa fuzzy matrix, Computers Math. Appl. 54 (2007)310–318.

1.6. Possibility theory

• P. Li, B. Liu, Entropy of credibility distributionsfor fuzzy variables, IEEE Trans. on Fuzzy Systems16(1) (2008) 123–129.

1.7. Fuzzy numbers and intervals1.7.1. Mathematical aspects

• S. Aytar, S. Pehlivan, Statistical convergence ofsequences of fuzzy numbers and sequences of �-cuts, Internat. J. of General Systems 37(2) (2008)231–238.

• Y.G. Zhu, On para-normed space with fuzzy vari-ables based on expected valued operator, Internat.J. of Uncertainty Fuzziness and Knowledge-BasedSystems 16(1) (2008) 95–106.

1.7.2. Computational aspects

• J. Fortin, D. Dubois, H. Fargier, Gradual num-bers and their application to fuzzy interval analy-sis, IEEE Trans. on Fuzzy Systems 16(2) (2008)388–402.

1.7.3. Ranking fuzzy intervals

• B. Asady, A. Zendehnam, Ranking fuzzy numbersby distance minimization, Appl. Math. Modelling31 (2007) 2589–2598.

• V.N. Huynh, Y. Nakamori, J. Lawry, A probability-based approach to comparison of fuzzy numbersand applications to target-oriented decision mak-ing, IEEE Trans. on Fuzzy Systems 16(2) (2008)371–387.

• K. Tenekedjiev, N.D. Nikolova, Ranking discreteoutcome alternatives with partially quantified un-certainty, Internat. J. of General Systems 37(2)(2008) 249–274.

2. Applications to pure and applied mathematics2.5. Generalized measure theory2.5.1. Nonadditive measures and integrals

• R. Mesiar, A. Mesiarová, Fuzzy integrals—Whatare they? MDAI 199–212.

2.5.3. Fuzzy random variables

• Y. Yoshida, Perception-based estimations of fuzzyrandom variables: linearity and convexity, Internat.J. of Uncertainty Fuzziness and Knowledge-BasedSystems 16(Suppl.) (2008) 71–88.

3. Information science and engineering3.1. Artificial intelligence3.1.2. Fuzzy rule-based expert systems

• H.E. Lee, K.H. Park, Z.Z. Bien, Iterative fuzzyclustering algorithm with supervision to constructprobabilistic fuzzy rule base from numerical data,IEEE Trans. on Fuzzy Systems 16(1) (2008)263–277.

3.1.3. Linguistic variables and perceptions

• F. Herrera, E. Herrera-Viedma, L. Martínez, Afuzzy linguistic methodology to deal with unbal-anced linguistic term sets, IEEE Trans. on FuzzySystems 16(2) (2008) 354–370.

3.1.8. Case-based reasoning

• V.C. Georgopoulos, C.D. Stylios, Complementarycase-based reasoning and competitive fuzzy cog-nitive maps for advanced medical decisions, SoftComput. 12(2) (2008) 191–200.

3.1.10. Temporal reasoning

• S. Schockaert, M. De Cock, E.E. Kerre, FuzzifyingAllen’s temporal interval relations, IEEE Trans. onFuzzy Systems 16(2) (2008) 517–533.

3.2. Machine learning and knowledge discovery3.2.1. Neural networks

• B.J. Park, W. Pedrycz, S.K. Oh, Simplified fuzzyinference rule-based genetically optimized hybridfuzzy neural networks, Internat. J. of UncertaintyFuzziness and Knowledge-Based Systems 16(2)(2008) 245–274.

708 Recent Literature / Fuzzy Sets and Systems 160 (2009) 706–713

3.2.6. Support vector machines

• J. Lu, X.W. Yang, G.Q. Zhang, Support vectormachine-based multi-source multi-attribute infor-mation integration for situation assessment, ExpertSystems Appl. 34 (2008) 1333–1340.

3.2.7. Data mining

• C.H. Chen, V.S. Tseng, T.P. Hong, Cluster-basedevaluation in fuzzy-genetic data mining, IEEETrans. on Fuzzy Systems 16(1) (2008) 249–262.

• C.M. Hung, Y.M. Huang, Conflict-sensitivity con-texture learning algorithm for mining interestingpatterns using neuro-fuzzy network with decisionrules, Expert Systems Appl. 34 (2008) 159–172.

• P.C.H. Ma, K.C.C. Chan, Inferring gene regula-tory networks from expression data by discover-ing fuzzy dependency relationships, IEEE Trans.on Fuzzy Systems 16(2) (2008) 455–465.

• A. Niewiadomski, A type-2 fuzzy approach to lin-guistic summarization of data, IEEE Trans. onFuzzy Systems 16(1) (2008) 198–212.

• T. Sudkamp, Sporatic fuzzy temporal associations,SCS 3–16.

3.3. Information systems3.3.1. Databases

• G. de Tré, R. de Caluwe, H. Prade, Null valuesin fuzzy databases, J. Intell. Inform. System 30(2008) 93–114.

3.3.3. Ontologies and the web semantics

• T. Lukasiewicz, Fuzzy description logic programsunder the answer set semantics for the semanticweb, Fund. Inform. 82 (2008) 289–310.

3.4. Computer science3.4.3. Formal languages and automata

• J.L. Grantner, P.A. Tamayo, R. Gottipati, G.A.Fodor, Design of a reconfigurable state transitionalgorithm for fuzzy automata, SCS 103–126.

3.4.6. Man-machine communication

• P.J.S. Gonçalves, L.F. Mendonça, J.M.C. Sousa,J.R.C. Pinto, Uncalibrated eye-to-hand visual ser-voing using inverse fuzzy models, IEEE Trans. onFuzzy Systems 16(2) (2008) 341–353.

3.5. Information processing3.5.1. Pattern recognition and classification

• J.D. Deng, C. Simmermacher, S. Cranefield,A study of feature analysis for musical instru-ment classification. IEEE Trans. on Systems,Man Cybernet.—Part B: Cybernet. 38(2) (2008)429–438.

• A. Ozturk, A. Arslan, F. Hardalac, Comparisonof neuro-fuzzy systems for classification of tran-scranial Doppler signals with their chaotic invari-ant measures, Expert Systems Appl. 34 (2008)1044–1055.

3.5.2. Clustering

• R.K. Brouwer, Fuzzy clustering of featuresvectors with some ordinal valued attributes usinggradient descent for learning, Internat. J. of Uncer-tainty Fuzziness and Knowledge-Based Systems16(2) (2008) 195–218.

• K. Mizutani, R. Inokuchi and S. Miyamoto, Algo-rithms of nonlinear document clustering based onfuzzy multiset model, MDAI 176–198.

3.5.3. Image processing and computer vision

• R. Muñoz-Salinas, E. Aguirre, O. Cordón, M.García-Silvente, Automatic tuning of a fuzzyvisual system using evolutionary algorithms:single-objective versus multiobjective approaches,IEEE Trans. on Fuzzy Systems 16(2) (2008)485–501.

• A. Sengur, Wavelet transform and adaptive neuro-fuzzy inference system for color texture classifica-tion, Expert Systems Appl. 34 (2008) 2120–2128.

• J.D. Wu, T.R. Chen, Development of a drowsi-ness warning system based on the fuzzy logic im-ages analysis, Expert Systems Appl. 34 (2008)1556–1561.

• J.Y. Yeh, J.C. Fu, A hierarchical genetic algo-rithm for segmentation of multi-spectral human-brain MRI, Expert Systems Appl. 34 (2008)1285–1295.

3.5.5. Signal processing

• G. Feng, M. Chen, D. Sun, T. Zhang, Approachesto robust filtering design of discrete time fuzzydynamic systems, IEEE Trans. on Fuzzy Systems16(2) (2008) 331–340.

Recent Literature / Fuzzy Sets and Systems 160 (2009) 706–713 709

3.5.6. Multiple source data fusion

• S. Nefti, M. Oussalah, U. Kaymak, A new fuzzyset merging technique using inclusion-based fuzzyclustering, IEEE Trans. on Fuzzy Systems 16(1)(2008) 145–161.

3.6. Statistics and data analysis3.6.2. Data analysis methods

• S. Abbasbandy, R. Ezzati, H. Behforooz, Inter-polation of fuzzy data by using fuzzy splines,Internat. J. of Uncertainty Fuzziness andKnowledge-Based Systems 16(1) (2008) 107–116.

3.6.3. Regression analysis

• C. Chakraborty, D. Chakraborty, Fuzzy linear andpolynomial regression modelling of ‘if-then’ fuzzyrulebase, Internat. J. of Uncertainty Fuzziness andKnowledge-Based Systems 16(2) (2008) 219–232.

• P.Y. Hao, J.H. Chiang, Fuzzy regression analysisby support vector learning approach, IEEE Trans.on Fuzzy Systems 16(2) (2008) 428–441.

• Y. Qiu, H. Yang, Y.Q. Zhang, Y. Zhao, Polyno-mial regression interval-valued fuzzy systems, SoftComput. 12(2) (2008) 137–146.

3.6.4. Forecasting

• R.A. Aliev, B. Fazlollahi, R.R. Aliev, B. Guirimov,Linguistic time series forecasting using fuzzy re-current neural network, Soft Comput. 12(2) (2008)183–190.

• T. Chen, Y.C. Lin, A fuzzy-neural system in-corporating unequally important expert opinionsfor semiconductor yield forecasting, Internat. J.of Uncertainty Fuzziness and Knowledge-BasedSystems 16(1) (2008) 35–58.

• C.H. Cheng, T.L. Chen, H.J. Teoh, C.H. Chiang,Fuzzy time-series based on adaptive expectationmodel for TAIEX forecasting, Expert SystemsAppl. 34 (2008) 1126–1132.

• C.H. Cheng, G.W. Cheng, J.W. Wang, Multi-attribute fuzzy time series method based onfuzzy clustering, Expert Systems Appl. 34 (2008)1235–1242.

• H.C.W. Lau, E.N.M. Cheng, C.K.M. Lee, G.T.S.Ho, A fuzzy logic approach to forecast energy con-sumption change in a manufacturing system, Ex-pert Systems with Appl. 34 (2008) 1813–1824.

• W. Stach, L.A. Kurgan, W. Pedrycz, Numericaland linguistic prediction of time series with the use

of fuzzy cognitive maps, IEEE Trans. on FuzzySystems 16(1) (2008) 61–72.

• R. Yang, Z. Wang, P.A. Heng, K.S. Leung, Fuzzi-fied Choquet integral with a fuzzy-valued inte-grand and its application to temperature prediction,IEEE Trans. on Systems, Man Cybernet.—Part B:Cybernet. 38(2) (2008) 367–380.

3.6.5. Reliability

• M. Sallak, C. Simon, J.F. Aubry, A fuzzy proba-bilistic approach for determining safety integritylevel, IEEE Trans. on Fuzzy Systems 16(1) (2008)239–248.

• H. Shu, Q. Liang, J. Gao, Wireless sensor networklifetime analysis using interval type-2 fuzzy logicsystems, IEEE Trans. on Fuzzy Systems 16(2)(2008) 416–427.

3.7. Fuzzy system modeling3.7.1. Modeling

• P. Angelov, E. Lughofer, Data-driven evolvingfuzzy systems using eTS and FLEXFIS: compar-ative analysis, SCS, 45–68.

• C.A. Gama, A.G. Evsukoff, P. Weber, N.F.F.Ebecken, Parameter identification of recurrentfuzzy systems with fuzzy finite-state automatarepresentation, IEEE Trans. on Fuzzy Systems16(1) (2008) 213–224.

3.7.3. Rule-based interpolation

• B.S. Chen, Y.T. Chang, Y.C. Wang, RobustH∞-stabilization design in gene networks understochastic molecular noises: fuzzy-interpolationapproach, IEEE Trans. on Systems, Man Cybernet.Part B: Cybernet. 38(1) (2008) 25–42.

• Z. Huang, Q. Shen, Fuzzy interpolation and ex-trapolation: a practical approach, IEEE Trans. onFuzzy Systems 16(1) (2008) 13–28.

3.7.4. Granular computing

• A. Bargiela, W. Pedrycz, Toward a theory of gran-ular computing for human-centered informationprocessing, IEEE Trans. on Fuzzy Systems 16(2)(2008) 320–330.

• M. Kudo, T. Murai, Extended DNF expressionand variable granularity in information tables,IEEE Trans. on Fuzzy Systems 16(2) (2008)285–298.

710 Recent Literature / Fuzzy Sets and Systems 160 (2009) 706–713

• F.E. Petry, R.R. Yager, Evidence resolution usingconcept hierarchies, IEEE Trans. on Fuzzy Sys-tems 16(2) (2008) 299–308.

• J.H. Wang, J.Y. Liang, Y.H. Qian, C.Y. Dang, Un-certainty measure of rough sets based on a knowl-edge granulation for incomplete information sys-tems, Internat. J. of Uncertainty Fuzziness andKnowledge-Based Systems 16(2) (2008) 233–244.

• S.Y. Wang, Applying 2-tuple multigranularity lin-guistic variables to determine the supply perfor-mance in dynamic environment based on product-oriented strategy, IEEE Trans. on Fuzzy Systems16(1) (2008) 29–39.

• Y.Q. Zhang, B. Jin, Y. Tang, Granular neuralnetworks with evolutionary interval learning,IEEE Trans. on Fuzzy Systems 16(2) (2008)309–319.

3.8. Fuzzy control3.8.1. Fuzzy PID control

• B.M. Mohan, A. Sinha, Analytical structures forfuzzy PID controllers? IEEE Trans. on Fuzzy Sys-tems 16(1) (2008) 52–60.

3.8.2. Robust control

• T.H.S. Li, S.H. Tsai, J.Z. Lee, M.Y. Hsiao, C.H.Chao, Robust H∞ fuzzy control for a class ofuncertain discrete fuzzy bilinear systems. IEEETrans. on Systems Man Cybernet.—Part B: Cy-bernet. 38(2) (2008) 510–527.

• C. Lin, Q.G. Wang, T.H. Lee, Y. He, Design ofobserver-based H∞ control for fuzzy time-delaysystems, IEEE Trans. on Fuzzy Systems, 16(2)(2008) 534–543.

• H.N. Wu and H.X. Li, H∞ fuzzy observer-basedcontrol for a class of nonlinear distributed param-eter systems with control constraints, IEEE Trans.on Fuzzy Systems 16(2) (2008) 502–516.

3.8.3. Sliding mode control

• Y.C. Chung, B.J. Wen, Y.C. Lin, Optimal fuzzysliding-mode control for bio-microfluidic ma-nipulation, Control Engrg. Practice 15 (2007)1093–1105.

• C.L. Hwang, N.W. Chang, Fuzzy decentralizedsliding-mode control of a car-like mobile robot indistributed sensor-network spaces, IEEE Trans. onFuzzy Systems 16(1) (2008) 97–109.

3.8.5. Stability analysis

• C.T. Pang, Y.Y. Lur, On the stability of Takagi–Sugeno fuzzy systems time-varying uncertain-ties, IEEE Trans. on Fuzzy Systems 16(1) (2008)162–170.

3.8.6. Adaptive fuzzy control

• C.J. Chien, A combined adaptive law for fuzzyiterative law for fuzzy iterative learning control ofnonlinear systemswith varying control tasks. IEEETrans. on Fuzzy Systems 16(1) (2008) 40–51.

• R. Shahnazi, M.R. Akbarzadeh-T., PI adaptivefuzzy control with large and fast disturbance re-jection for a class of uncertain nonlinear systems,IEEE Trans. on Fuzzy Systems 16(1) (2008)187–197.

• R.J. Wai, M.A. Kuo, J.D. Lee, Cascade directadaptive fuzzy control design for a nonlinear two-axis inverted-pendulum servomechanism, IEEETrans. on Systems Man Cybernet.—Part B: Cy-bernet. 38(2) (2008) 439–454.

3.8.7. Controller design

• X.Y. Du, N.Y. Zhang, H. Ying, Structure analysisand system design for a class of Mamdani fuzzycontrollers, SCS 83–102.

• H. Zhang, Y.Wang, D. Liu, Delay-dependent guar-anteed cost control for uncertain stochastic fuzzysystems with multiple time delays. IEEE Trans. onSystems, Man and Cybernet.—Part B: Cybernet.38(1) (2008) 126–140.

3.8.8. Tracking

• I. Turkmen, IMM fuzzy probabilistic data associ-ation algorithm for tracking maneuvering target.Expert Systems Appl. 34 (2008) 1243–1249.

3.9. Decision sciences3.9.1. Preference modeling

• S. Alonso, F. Chiclana, F. Herrera, E. Herrera-Viedma, J. Alcalá-Fdez, C. Porcet, A consistency-based procedure to estimate missing pairwisepreference values, MDAI 155–175.

3.9.2. Multiple criteria decision-making

• G.T. Fu, A fuzzy optimization method for mul-ticriteria decision making: an application to

Recent Literature / Fuzzy Sets and Systems 160 (2009) 706–713 711

reservoir flood control operation, Expert Systemswith Applications 34 (2008) 145–149.

• I. Kojadinovic, Unsupervised aggregation of com-mensurate correlated attributes by means of theChoquet integral and entropy functionals, MDAI128–154.

• A.H.I. Lee, W.C. Chen, C.J. Chang, A fuzzy AHPand BSC approach for evaluating performanceof IT department in the manufacturing industryin Taiwan, Expert Systems with Appl. 34 (2008)96–107.

• Y. Narukawa, T. Murofushi, Choquet-Stieltjes in-tegral as a tool for decision modelling, MDAI115–127.

• S. Sandri, C. Sibertin-Blanc, A multicriteria sys-tem using fuzzy gradual rule bases and fuzzyarithmetic, Internat. J. of Uncertainty Fuzzinessand Knowledge-Based Systems 16(Suppl.) (2008)17–34.

• H.Y. Wang, S.M. Chen, Evaluating students’ an-swerscripts using fuzzy numbers associated withdegrees of confidence, IEEE Trans. on Fuzzy Sys-tems 16(2) (2008) 403–415.

3.9.3. Group decision making

• Y.P. Jiang, Z.P. Fan, An approach to group deci-sion making based on incomplete fuzzy preferencerelations, Internat. J. of Uncertainty Fuzziness andKnowledge-Based Systems 16(1) (2008) 83–94.

• C.J. Lin, W.W. Wu, A causal analytical method forgroup decision-making under fuzzy environment,Expert Systems Appl. 34 (2008) 205–213.

• J. Montero, The impact of fuzziness in socialchoice paradoxes, Soft Comput. 12(2) (2008)177–182.

3.9.5. Game theory

• D. Butnariu, T. Kroupa, Shapley mappings and thecumulative value of n-person games with fuzzycoalitions, European J. Oper. Res.186 (2008)288–299.

• W. Dwayne Collins, C. Hu, Studying interval val-ued matrix games with fuzzy logic, Soft Comput.12(2) (2008) 147–156.

• L. Sacconi, S. Moretti, A fuzzy logic anddefault reasoning model of social norms andequilibrium selection in games under unforeseencontingencies, Internat. J. of Uncertainty Fuzzi-ness and Knowledge-Based Systems 16(1) (2008)59–82.

3.9.6. Risk analysis

• L. Qi, X. Jia, D. Yong, A subjective methodologyfor risk quantification based on generalized fuzzynumbers, Internat. J. of General Systems 37(2)(2008) 149–166.

3.10. Optimization3.10.3. Multiple criteria optimization

• C.F. Hu, C.J. Teng, S.Y. Li, A fuzzy goal pro-gramming approach to multi-objective optimiza-tion problem with priorities, European J. Oper.Res. 176 (2008) 1319–1333.

3.10.4. Combinatorial optimization

• F.T. Lin, Solving the knapsack problem withimprecise weight coefficients using genetic al-gorithms. European J. Oper. Res. 185 (2008)133–145.

• H.S. Wang, Configuration change assessment: ge-netic optimization approach with fuzzy multiplecriteria for part supplier selection decisions, Ex-pert Systems Appl. 34 (2008) 1541–1555.

4. Applications4.1. Engineering4.1.1. Process engineering

• C.W. Chang, C.R. Wu, H.C. Chen, Using experttechnology to select unstable slicing machine tocontrol wafer slicing quality via fuzzy AHP, ExpertSystems Appl. 34 (2008) 2210–2220.

• C.K. Kwong, K.Y. Chan, H.Wong, Takagi–Sugenoneural fuzzy modeling approach to fuild dispens-ing for electronic packaging, Expert SystemsAppl. 34 (2008) 2111–2119.

4.1.2. Fault detection and troubleshooting

• K.L. Hsieh, The application of clustering analysisfor the critical areas on LFT-LCD panel, ExpertSystems Appl. 34 (2008) 952–957.

• C.H. Wang, Recognition of semiconductor defectpatterns using spatial filtering and spectral cluster-ing, Expert Systems Appl. 34 (2008) 1914–1923.

4.1.3. Production research

• R. Felix, Real world applications of a fuzzydecision model based on relationships between

712 Recent Literature / Fuzzy Sets and Systems 160 (2009) 706–713

goals for finance services and sequencing forthe assembly of cars, Soft Comput. 12(2) (2008)129–136.

• B.M. Hsu, M.H. Shu, Fuzzy inference to assessmanufacturing process capability with imprecisedata, European J. Oper. Res. 186 (2008) 652–670.

• H.T. Lin, W.L. Chang, Order selection and pric-ing methods using flexible quantity and fuzzy ap-proach for buyer evaluation, European J. Oper.Res. 187 (2008) 415–428.

• D. Petrovic, Y. Xi, K. Turnham, R. Petrovic, Co-ordinated control of distribution supply chains inthe presence of fuzzy customer demand, EuropeanJ. Oper. Res. 185 (2008) 146–158.

• A.Y. Rong, R. Lahdelma, Fuzzy chance con-strained linear programming model for optimizingthe scrap charge in steel production, European J.Oper. Res. 186 (2008) 953–964.

4.1.4. Robotics and mechanical engineering

• C.S. Chiu, K.Y. Lian, Hybrid fuzzy model-basedcontrol of nonholonomic systems: a unified view-point, IEEE Trans. on Fuzzy Systems 16(1) (2008)85–96.

4.1.5. Management of large scale systems

• C.F. Lee, S.J. Lin, C. Lewis, Y.F. Chang, Ef-fects of carbon taxes on different industries byfuzzy goal programming: a case study of thepetrochemical-related industries, Taiwan, EnergyPolicy 35 (2007) 4051–4058.

4.1.6. Civil engineering and earth sciences

• Y.M. Kim, C.K. Kim, G.H. Hong, Fuzzy setbased crack diagnosis system for reinforced con-crete structures, Comput. Structures 85 (2007)1828–1844.

• D.A. Shook, P.N. Roschke, P.Y. Lin, C.H. Loh,GA-optimized fuzzy logic control of a large-scalebuilding for seismic loads, Engrg. Structures 30(2008) 436–449.

4.1.10. Agricultural and environmental engineering

• A.A. Barreto-Neto, C.R. de Souza Filho, Appli-cation of fuzzy logic to the evaluation of runoffin a tropical watershed, Environmental ModellingSoftware 23 (2008) 244–253.

4.1.12. Telecommunications and computer networks

• A. Abadpour, A.S. Alfa, J. Diamond, Video-on-demand network design and maintenance usingfuzzy optimization. IEEE Trans. on SystemsMan Cybernet.—Part B: Cybernet. 38(2) (2008)404–420.

• B. Melián, J.L. Verdegay, Fuzzy optimiza-tion models for the design of WDM networks,IEEE Trans. on Fuzzy Systems 16(2) (2008)466–476.

4.1.13. Electrical engineering

• C. Elmas, O. Ustun, H.H. Sayan, A neuro-fuzzycontroller for speed control of a permanent magnetsynchronous motor drive, Expert Systems Appl.34 (2008) 657–664.

• G.H. Hwang, D.W. Kim, J.H. Lee, Y.J. An, De-sign of fuzzy power system stabilizer using adap-tive evolutionary algorithm, Eng. Appl. ArtificialIntelligence 21 (2008) 86–96.

4.1.14. Military applications

• A. Mendez-Vazquez, P. Gader, J.M. Keller, K.Chamberlin, Minimum classification error trainingfor Choquet integrals with applications to land-mine detection, IEEE Trans. on Fuzzy Systems16(1) (2008) 225–238.

4.2. Medicine

• K. Polat, S. Günes, Artificial immune recognitionsystem with fuzzy resource allocation mechanismclassifier, principal component analysis and FFTmethod based new hybrid automated identificationsystem for classification of EEG signals, ExpertSystems Appl. 34 (2008) 2039–2048.

• K. Polat, S. Günes, S. Tosun, Diagnosis of heartdisease using artificial immune recognition systemand fuzzy weighted pre-processing, Pattern Recog-nition 39 (2006) 2186–2193.

• E.D. Übeyli, Adaptive neuro-fuzzy inference sys-tem employing wavelet coefficients for detectionof ophthalmic arterial disorders. Expert SystemsAppl. 34 (2008) 2201–2209.

Recent Literature / Fuzzy Sets and Systems 160 (2009) 706–713 713

4.3. Economics4.3.2. Finance and marketing science

• G.S. Ng, C. Quek, H. Jiang, FCMAC-EWS: a bankearly warning system based on a novel localizedpattern learning and semantically associative fuzzyneural network, Expert Systems Appl. 34 (2008)989–1003.

4.4. Behavioral and social sciences4.4.4. E-learning

• S.S. Tseng, P.C. Sue, J.M. Su, J.F. Weng, W.N.Tsai, A new approach for constructing the conceptmap, Comput. Education 49 (2007) 691–707.