Interpretability and Learning in Neuro-Fuzzy Systems

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Year 2015 : 6 citations

 A New Fuzzy Neural System with Applications
Y Chai, J Chen, W Luo - books.google.com
Abstract. Through a comprehensive study of existing fuzzy neural systems, this paper
presents a Choquet integral-OWA operator based fuzzy neural system named AggFNS as a
new hybrid method of CI, which has advantages in universal fuzzy inference operators ...

 A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification
K ?apa, K Cpa?ka, AI Galushkin - Artificial Intelligence and Soft Computing, 2015 - Springer
Abstract In this paper a new approach for construction of neuro-fuzzy systems for nonlinear
classification is introduced. In particular, we concentrate on the flexible neuro-fuzzy systems
which allow us to extend notation of rules with weights of fuzzy sets. The proposed ..

 Fuzzy rule base simplification using multidimensional scaling and constrained optimization
GE Tsekouras - Fuzzy Sets and Systems, 2015 - Elsevier
Abstract This paper proposes a novel approach for the development of highly accurate and
interpretable fuzzy models with Gaussian fuzzy sets, under the framework of
multidimensional scaling and non-linear constrained optimization. Upon the assumption ..

 Establishment of EEG Index Related to Calmness
SAM Aris, N Sulaiman, MN Taib - … Science, Engineering and …, 2015 - ingentaconnect.com
Often, individuals with mental problems will be diagnosed via interview by the medical
practitioners and will only refer to a specialist if the condition worsens. It will take some time
and long period of diagnosis is required. This research is one of the stages in proposing ...

 Identification and on-Line Validation of a pH Neutralization Process Using an Adaptive Network Based Fuzzy Inference System
AS Mota, MR Menezes, JE Schmitz… - Chemical …, 2015 - Taylor & Francis
ABSTRACT In this study, the application of Adaptive Neuro-Fuzzy Inference System
architecture to build prediction models that represent the pH neutralization process is
proposed. The dataset used to identify the process was obtained experimentally in a ...

 A Novelty Detection Framework Based on Fuzzy Entropy for a Complex Manufacturing Process
A Gonzalez-Rodriguez, G Panoutsos, M Mahfouf… - Intelligent Systems' …, 2015 - Springer
Abstract In this paper a new Novelty Detection framework is presented, which is created by
taking advantage of the Fuzzy Entropy property of Fuzzy Logic systems. The framework's
aim is to create a linguistic-based feedback mechanism for advising the process users ( ...

Year 2014 : 4 citations

 Ovarian cancer diagnosis using a hybrid intelligent system with simple yet convincing rules
D Wang, C Quek, GS Ng - Applied Soft Computing, 2014 - Elsevier
Abstract Ovarian cancer is the ninth most common cancer among women and ranks fifth in
cancer deaths. Statistics show that the five-year survival rate is greater than 75% if diagnosis
occurs before the cancer cells have spread to other organs (stage I), but it drops to 20% ...

 Open-loop neuro-fuzzy speed estimator applied to vector and scalar induction motor drives
F Lima, W Kaiser, IN da Silva, AAA de Oliveira - Applied Soft Computing, 2014 - Elsevier
Abstract Scalar and vector drives have been the cornerstones of control of industrial motors
for decades. In both the elimination of mechanical speed sensor consists in a trend of
modern drives. This work proposes the development of an adaptive neuro-fuzzy inference ...

 Particle swarm optimization for ANFIS interpretability and accuracy
DP Rini, SM Shamsuddin, SS Yuhaniz - Soft Computing, 2014 - Springer
Abstract The strength of the adaptive neuro-fuzzy system (ANFIS) involves two contradictory
requirements in a common fuzzy modeling problem, ie interpretability and accuracy. It is
known that simultaneous optimization of accuracy and interpretability will improve ...

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??? ??? ??? - ??????????, 2014 - cqvip.com
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???????????, ????????????????. ?????????????
???????, ???????, ?????????????. ???4 ??????? ...

Year 2013 : 8 citations

 Chen, Chuen-Jyh, Shih-Ming Yang, and Chu-Yun Chen (2013). "Development of a rule selection mechanism by using neuro-fuzzy methodology for structural vibration suppression." Journal of Intelligent and Fuzzy Systems.

 J Contreras (2013). “Generating Singleton Fuzzy Models from Data with Interpretable Partition”, Advanced Materials Research, 2013, Volume 629, pp. 784-791

 Jazebi, Fateme, and Abbas Rashidi. "An automated procedure for selecting project managers in construction firms." Journal of Civil Engineering and Management 19.1 (2013): 97-106.

 Al-Jamimi, Hamdi A.; Ahmed, Moataz, "Machine Learning-Based Software Quality Prediction Models: State of the Art," Information Science and Applications (ICISA), 2013 International Conference on , vol., no., pp.1,4, 24-26 June 2013

 Juang, C.-F.; Juang, K.-J., "Reduced Interval Type-2 Neural Fuzzy System Using Weighted Bound-Set Boundary Operation for Computation Speedup and Chip Implementation," Fuzzy Systems, IEEE Transactions on , vol.21, no.3, pp.477,491, June 2013

 LAO Chao-hui; WU Qun-qi; WANG Fang (2013). “Division method of node level for highway network”, Traffic and Transportation Engineering, 2, 80-85

 K. VIJAYA SRI, K. USHA RANI (2013), “NEURO-FUZZY SYSTEMS AND APPLICATIONS – A REVIEW”, PUBLICATIONS OF PROBLEMS & APPLICATION IN ENGINEERING RESEARCH, Vol 04, Special Issue01; 2013

 Shamsuddin, S. M., Rini, D. P., & Yuhaniz, S. S. (2013). Balanced the Trade-offs problem of ANFIS Using Particle Swarm Optimisation. AWERProcedia Information Technology and Computer Science, 4(2).

Year 2012 : 9 citations

 RJ Alitappeh, KJ Saravi, F Mahmoudi (2012). “A New Illumination Invariant Feature Based on SIFT Descriptor in Color Space”, Procedia Engineering, Volume 41, 2012, Pages 305–311

 Carrillo S., Contreras J., Vergara E. (2012?), “UNA NUEVA TÉCNICA PARA IDENTIFICACIÓN DE EMBARCACIONES”. Technical Report.

 Juan, Contreras; William, Cuadrado; David, Munoz; George, Archbold; Delgado; Delgado, Geraldine; Vladimir, Diaz, "Automatic ship hull inspection using fuzzy logic," Applied Imagery Pattern Recognition Workshop (AIPR), 2012 IEEE , vol., no., pp.1,5, 9-11 Oct. 2012, doi: 10.1109/AIPR.2012.6528214

 Juang, C.-F.; Chen, C.-Y., (2012). "Data-Driven Interval Type-2 Neural Fuzzy System With High Learning Accuracy and Improved Model Interpretability," Cybernetics, IEEE Transactions on , vol.PP, no.99, pp.1,15, 0

 SJ Lee, XJ Zeng (2012). “A similarity-based learning algorithm for fuzzy system identification with a two-layer optimization scheme”, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

 F Lima, W Kaiser, I Nunes da Silva, A A de Oliveira (2012). “Speed Neuro-fuzzy Estimator Applied To Sensorless Induction Motor Control”, IEEE Latin America Transactions, Vol 10, Issue: 5, pp. 2065 – 2073

 Rezaee, Babak. "Rule base simplification by using a similarity measure of fuzzy sets." Journal of Intelligent and Fuzzy Systems 23.5 (2012): 193-201.

 Solis, Adrian Rubio, and George Panoutsos. "Granular Computing Neural-Fuzzy Modelling: A Neutrosophic Approach." Applied Soft Computing (2012).

 Rosemann, N. (2012). Beherrschbares Online-Lernen durch inkrementelle, lokale Regularisierung.

Year 2011 : 8 citations

 Contreras J., Muñoz D. (2011). “Algoritmo para generación de Controladores Difusos Interpretables. Aplicación a un proceso de presión”, ITECKNE, Vol. 8, No. 2, pp. 177-182

 Herrera L.J., Pomares H., Rojas I., Guillén A. Awad, M. and Valenzuela, O. (2011). “The TaSe-NF model for function approximation problems: Approaching local and global modelling”. Fuzzy Sets and Systems, Vol. 171 (1), pp. 1-21.

 Liang Hui; Tong Chaonan; Peng Kaixiang; , "Data-driven modeling and online algorithm for hot rolling process," Control Conference (CCC), 2011 30th Chinese , vol., no., pp.1560-1564, 22-24 July 2011

 CF Juang, KJ Juang (2011). “Reduced Interval Type-2 Neural Fuzzy System Using Weighted Bound-Set Boundary Operation for Computation Speedup and Chip Implementation”, IEEE Transactions on Fuzzy Systems (accepted).

 Jianfeng Liu; Weihua Gui; Zhiwu Huang; Youmei Liu; Yuxiang Liu; , "Modelling and model optimization of locomotive brake control system," Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on , vol., no., pp.1256-1260, 16-18 Dec. 2011

 Mohamed, Raja Ben, Hichem Ben Nasr, and Faouzi M'Sahli. "A multimodel approach for a nonlinear system based on neural network validity." International Journal of Intelligent Computing and Cybernetics 4.3 (2011): 331-352.

 Torun Y. and Tohumo?lu G. (2011). “Designing simulated annealing and subtractive clustering based fuzzy classifier”. Applied Soft Computing, Vol. 11( 2), pp. 2193-2201

 Wróbel, Micha?. (2011). "Zastosowanie neuronowych systemów rozmytych w chemii.". PhD Thesis, University of Katowice, Poland.

Year 2010 : 8 citations

 Bai, R., Tong, S.-C., Chai, T.-Y. Kongzhi yu Juece (2010). “Fuzzy rules extraction from process data for operation control of the raw slurry blending process”, Control and Decision 25 (7), pp. 1015-1020. ??, ???, and ???. "??????????????????????." ????? 25.7 (2010).

 Gegov A. (2010). “Fuzzy Netwroks for Complex Systems”. Springer.

 Herrera L.J., Pomares H., Rojas I., Guillén A. Awad, M. and Valenzuela, O. (2010). “The TaSe-NF model for function approximation problems: Approaching local and global modelling”. Fuzzy Sets and Systems (in press).

 Li S.-T., Kuo S.-C. And Tsai F.-C. (2010). “An intelligent decision-support model using FSOM and rule extraction for crime prevention”. Expert Systems with Applications, Vol. 37(10), pp. 7108-7119.

 Contreras J. (2010). “IDENTIFICATION AND FUZZY CONTROL OF A TWO TANK SYSTEM”. Revista Colombiana de Tecnologías de Avanzada., Vol. 2(16), pp. 43-49

 Contreras J., Martinez L. B., Puerta Y. V. (2010). “Clasificador Difuso para Diagnóstico de Enfermedades”. Revista Tecno Lógicas, No. 25, pp. 201-220.

 Vélez, MA; Sanchez, O; Romero, S; Andujar, JM (2010). “A new methodology to improve interpretability in neuro-fuzzy TSK models”. APPLIED SOFT COMPUTING 10 (2): 578-591 MAR 2010.

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Year 2009 : 16 citations

 Cifuentes F. J., Gonzáles H. G. León, Contreras J. (2009). “GENERACIÓN AUTOMÁTICA DE CONTROLADORES BORROSO INTERPRETABLES PARA REGULACIÓN DE PROFUNDIDAD DE VEHÍCULO SUBACUÁTICO OPERADO REMOTAMENTE ROV”. Copinaval 2009.

 Contreras J., Acuña O. (2009). “Generating Interpretable Fuzzy Systems for Classification Problems”, Tecno Lógicas, Vol. 23.

 Contreras J., Acuna O. (2009). “Generating Dynamic Fuzzy Models for Prediction Problems”. 2009 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY, pp. 214-219.

 Forero et al. (2009). “GENERACIÓN AUTOMÁTICA DE CONTROLADORES DIFUSOS: APLICACIÓN AL CONTROL DE PROFUNDIDAD DE UN ROV”. 2 Congreso Internacional de Ingeniería Mecatrónica – UNAB.

 GIL NAVIA, F., CONTRERAS, J. (2009). Fuzzy Predictive Model of the Vertical Acceleration of a High Speed Vessel in Pitch Motion. Ship Science & Technology, North America, 3, Jan. 2010. Available at: . Date accessed: 20 Mar. 2012.

 Huang Y.-R., Kang Y., Chu M.-H., Chien S.-Y., Chang, Y.-P. (2009). “Modified recurrent neuro-fuzzy network for modeling ball-screw servomechanism by using Chebyshev polynomial”. Expert Systems with Applications Vol. 36 (3 PART 1), pp. 5317-5326.

 Iphar, M; Yavuz, M; Ak, H (2009). “Reply to the comment on "Prediction of ground vibrations resulting from the blasting operations in an open pit mine by adaptive neuro-fuzzy inference system" by Tarkan Erdik”. ENVIRONMENTAL EARTH SCIENCES 59 (2): 473-476 NOV 2009.

 Jiang W., Cui H. and Chen J. (2009). “A fuzzy modeling based dynamic resource allocation strategy in service grid”. Journal of China Universities of Posts and Telecommunications, Vol. 16 (1), pp. 108-113.

 Kolman E. and Margalioty M. (2009). “Knowledge-Based Neurocomputing: A Fuzzy Logic Approach”. Springer.

 Kolman E., Margaliot M. (2009). “Studies in Fuzziness and Soft Computing” 234, pp. 77-81, 89-98

 Leng G., Zeng, X. J. and Keane J. A. (2009). “A hybrid learning algorithm with a similarity-based pruning strategy for self-adaptive neuro-fuzzy systems”. Applied Sof Computing, Vol. 9, No. 4, pp. 1354-1366.

 Liang X, Pedrycz W. (2009). “Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms”. Fuzzy Sets and Systems 160 (24): 3475-3502 DEC 16 2009.

 Lu L., Li K-H, Guan Y L (2009). "Blind Detection of Interleaver Parameters for Non-Binary Coded Data Streams". IEEE International Conference on Communications, 2009 - ICC '09.

 Lu N., Zhou J. (2009). “Particle Swarm Optimization-Based RBF Neural Network Load Forecasting Model”. Power and Energy Engineering Conference - APPEEC 2009.

 Ma Y. and Song M. (2009). “A novel enhanced authentication scheme supporting forwarding mode in heterogeneous hierarchical mobile IPv6 networks”. Journal of China Universities of Posts and Telecommunications, Vol. 16 (1), pp. 86-91.

 ???, ??, and ??. "?????????????????." ?????? 25.1 (2009): 168-170.

Year 2008 : 19 citations

 Apolloni B. Pedrycz W. Bassis S. Malchiodi D. (2008). “Identifying Fuzzy Rules” in “The Puzzle of Granular Computing”, pp. 385-408.

 Choi J.-N., Kim H.-K., Oh S.-K. (2008). “Optimization of FCM-based radial basis function neural network using particle swarm optimization”. Transactions of the Korean Institute of Electrical Engineers, Vol. 57, No. 11, pp. 2108-2116.

 Contreras J., Llorea R. M. and Fernandez L. F. M. (2008). “Obtención de Modelos Borrosos Interpretables de Procesos Dinámicos”. Revista Iberoamericana de Automatica e Informática Industrial, Vol. 5, No. 3, pp. 70-77.

 Contreras J. (2008). “Diagnóstico del cáncer de mama empleando clasificador difuso”. Energya y Computación, Vol. 16(1), pp. 51-57.

 Duque W. (2008). “On the development of decision-making systems based on fuzzy models to assess water quality in rivers”. PhD Thesis, Universitat Rovira i Virgili, Tarragona, Spain.

 Eftekhari M. and Katebi, S. D. (2008). “Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter”. Applied Mathematical Modelling, Vol. 32, No. 12, 2634-2651.

 Eftekhari M., Katebi S. D., Karimi M. and Jahanmiri A. H. (2008). “Eliciting Transparent Fuzzy Model using Differential Evolution”. Applied Soft Computing, Vol. 8, No. 1., pp. 466-476.

 Hong C.-M., Lin C.-T., Huang C.-Y. and Lin Y.-M. (2008). “An Intelligent Fuzzy-Neural Diagnostic System for Osteoporosis Risk Assessment”, Proceedings of the World Academy of Science, Engineering and Technology, Vol 32.

 Huang Y.-R., Kang Y., Chu M.-H., Chang, Y.-P. (2008). “Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network”. Journal of Advanced Mechanical Design, Systems, and Manufacturing, pp. 949-960.

 Juang C. F, Tsao Y. W. (2008). “A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning”. IEEE Transactions on Fuzzy Systems, Vol. 16, No. 6, pp. 1411-1424.

 Pedrycz W. (2008). “Fuzzy Sets as User-Centric Processing Framework of Granular Computing“, in eds. Pedrycz W., Skowron A. Kreinovich V., Handbook of Granular Computing”. John Wiley & Sons.

 Pulkkinen P., Hytönen J. and Koivisto H. (2008). “Developing a bioaerosol detector using hybrid genetic fuzzy systems”. Engineering Applications of Artificial Intelligence, Vol. 21, No. (8), pp. 1330-1346.

 Ruiz I. Y. (2008). “A Global Approach for Supporting Operators’ Decision-Making Dealing with Plant Abnormal Events”. PhD Thesis, Departament D’Enginerya Química, Universitat Politècnica de Catalunya.

 Xing Z.-Y., Zhang Y. and Hou Y.-L., (2008). “Multi-Objective Fuzzy Modeling Using NSGA-II”. IEEE International Conference on Cybernetic Intelligent Systems (CIS 2008), China.

 Yazdani S. , Shoorehdeli M. A., Teshnehlab M. (2008). “Identification of Fuzzy Models Using Cartesian Genetic Programming”. International Conference on Computational-Intelligence and Security, pp. 639-644, China.

 Yélamos Ruiz, I. (2008). “A global approach for supporting operators' decision-making dealing with plant abnormal events”. PhD Thesis, Universitat Politécnica de Catalunya.

 Zhou, S.-M., Gan, J.Q. (2008). “Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling”. Fuzzy Sets and Systems, Vol. 159, No. 23, pp. 3091-3131.

 Zoumponos G. T. and Aspragathos N. A. (2008). “Fuzzy Logic Path Planning for the Robotic Placement of Fabrics on a Work Table”. Robotics and Computer-Integrated Manufacturing, Vol. 24, No. 2, pp. 174-186.

 ???, and ???. "??????????????????????????????????." ???????? (?????) 34.3 (2008): 2008-O6.

Year 2007 : 20 citations

 Bai R., Chai T. and Ma E. (2007). “A Novel Approach for Extraction of Fuzzy Rules Using the Neuro-fuzzy Network and Its Application in the Blending Process of Raw Slurry”. Advances in Neural Networks – ISNN 2007, Lecture Notes in Computer Science, Vol. 4492, pp. 362-370.

 Barros J.-C. and Dexter A. L. (2007). “On-line Identification of Computationally Undemanding Evolving Fuzzy Models”, Fuzzy Sets and Systems, Vol. 158, No. 18, pp. 1997-2012.

 Van Broekhoven, Ester. Monotonicity aspects of linguistic fuzzy models. Diss. Ghent University, 2007.

 Eftekhari M. and Katebi S. D. (2007). “Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter”. Applied Mathematical Computing, Vol. 32, No. 12, pp. 2634-2651.

 Kolman E. and Margalioty M. (2007). “Knowledge Extraction from Neural Networks using the All-Permutations Fuzzy Rule Base: the LED display recognition problem”. IEEE Transactions on Neural Networks, Vol. 18, No. 3, pp. 925-931.

 Lohani A. K., Goel N. K. and Bhatia K. K. S. (2007). “Reply to Comments provided by Z. Sen on “Takagi-Sugeno fuzzy inference system for modeling stage-discharge relationship”, Journal of Hydrology, Vol. 337. No. 1-2, pp. 244-247.

 Contreras J. et al. (2007). “Efficient Fuzzy Identification based on Inference Error”. Revista Colombiana de Tecnologías de Avanzada, Vol. 1, No. 9, pp. 1692-7257.

 Contreras J. (2007). “Algoritmos para Identificación de Modelos Difusos Interpretables”. IEEE Latin América Transactions, Vol. 5, No. 5, pp. 346-351.

 Contreras J., Llorca R. M. and Vivanco L. U. (2007). “Data-Driven Identification Algorithms for Automatic Determination of Interpretable Fuzzy Models”. IEEE Latin America Transactions, Vol. 5, No. 5, pp. 346-351.

 Contreras J., Llorca R. M., Fernandez L. M. (2007). “Interpretable fuzzy models from data and adaptive fuzzy control: A new approach”. IEEE International Conference on Fuzzy Systems, London, England.

 Contreras J. , Paz J. P., Amaya D. and Pineda A. (2007). “Realistic Ecosystem Modelling with Fuzzy Cognitive Maps”. International Journal of Computational Intelligence Research, Vol. 3, No. 2, pp. 139-144.

 Pedrycz W., Gomide F. (2007). “Fuzzy Modeling: Principles and Methodology”, in Fuzzy Systems Engineering, John Wiley and Sons.

 Xing Z.-Y., Hou Y.-L., Zhang Y. and Jia L.-M. (2007). “A Multi-Objective GA-based Fuzzy Modeling Approach for Constructing Pareto-optimal Fuzzy systems”, International Journal of Computer Science and Network Security, Vol. 6, No. 5, pp. 213-219.

 Xing Z.-Y., Zhang Y., Hou Y.-L. and Jia L.-M. (2007). “On Generating Fuzzy Systems based on Pareto Multi-Objective Cooperative Coevolutionary Algorithm”, International Journal of Control Automation and Systems, Vol. 5, No. 4, pp. 444-455.

 Zhang Y., Wu X.-B., Xiang Z.-R. and Hu W.-L. (2007). “Design of Complex Fuzzy Classification System Based on Cooperative Coevolutionary Algorithm”, CEPS, Vol. 24, No. 1, pp. 32-38.

 Zhang Y., Wu X.-B. and Xiang Z.-R. (2007). “Design of Interpretable Fuzzy Model Based on Clustering and Genetic Algorithm”, Computer Engineering, Vol. 33, No. 8. ??, et al. "???????????????????." ????? 33.8 (2007): 160-162.

 Zoumponos G. T. and Aspragathos N. A. (2007). “Vision aided neuro-fuzzy control for the folding of fabric sheets”. International Conference on Control, Automation and Systems (ICCAS '07).

 ??, et al. "?????????????????." ??????? 24.1 (2007): 725-731.

 ??, et al. "?? Pareto ??????????????." ???????? (2007).

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Year 2006 : 16 citations

 Alonso M, Guillaume S, Magdalena L (2006). “A hierarchical fuzzy system for assessing interpretability of linguistic knowledge bases in classification problems”. Information Processing and Management of Uncertainty in KnowledgeBased Systems - IPMU 2006.

 Chen, C.-M., Hong, C.-M., Chen, S.-Y., Liu, C.-Y. (2006). “Mining Formative Evaluation Rules Using Web-Based Learning Portfolios for Web-Based Learning Systems”. Educational Technology and Society, Vol. 9(3), pp. 69-87.

 Chen J.-L., Hou Y.-L., Xing Z.-Y, Jia L.-M. and Tong Z.-Z. (2006). “A Multi-Objective Genetic-based Method for Design Fuzzy Classification Systems”, International Journal of Computer Science and Network Security, Vol. 6, No. 8, pp. 110-117.

 Herrera L.J., Pomares H., Rojas I., Guilén A. Awad, M. and González, J. (2006). “Interpretable Rule Extraction and Function Approximation from Numerical Input/Output Data Using the Modified Fuzzy TSK Model, TaSe Model”. Proceedings of SPIE - The International Society for Optical Engineering, pp. 402-411.

 Hong C. M., Chen C. M., Chen S.Y., et al. (2006). “A novel and efficient neuro-fuzzy classifier for medical diagnosis”. IEEE International Joint Conference on Neural Network, Canada.

 Khosrow-Pour M. (2006). Emerging Trends And Challenges in Information Technology Management. IGI Publishing.

 Kumar A. (2006). “Interpretability and Mean-Square Error Performance of Fuzzy Inference Systems for Data Mining”. Intelligent Systems in Accounting, Finance and Management, Vol. 13, No. 4, pp. 185-196.

 Montes J. C., Llorca R. M. and Grau J. P. (2006). “Building Interpretable Fuzzy Systems: a New Approach to Fuzzy Modeling”. Electronics, Robotics and Automotive Mechanics Conference - CERMA'06, pp. 117-122.

 Musulin E., Yelamos I. and Puigjaner, L. (2006). “Integration of Principal Component Analysis and Fuzzy Logic Systems for Comprehensive Process Fault Detection and Diagnosis”. Industrial and Engineering Chemistry Research, Vol. 45(5), pp. 1739-1750.

 Pedrycz W., Reformat M. and Li K. (2006). “OR/AND Neurons and the Development of Interpretable Logic Models”. IEEE Transactions on Neural Networks, Vol. 17(3), pp. 636-658.

 Xing Z. Y., Hou Y. L., Tong Z. Z., et al. (2006). “Construction of fuzzy classification system based on multi-objective genetic algorithm”. 6th International Conference on Intelligent Systems Design and Applications (ISDA 2006), China.

 Xing Z. Y., Hou Y. L., Zhang Y., et al. (2006). “Construction of interpretable and precise fuzzy models using fuzzy clustering and multi-objective genetic algorithm”. 5th International Conference on Machine Learning and Cybernetics, China.

 Zhang Y., Xing, Z.-Y., Hu, W.-L. and Xiang, Z.-R. (2006). “Design of TS Fuzzy Model Based on Cooperative Evolutionary Algorithm”, Information and Control, Vol. 35, No. 4, pp. 480-486.

 Zhang Y., Xing, Z.-Y., Xiang, Z.-R. and Hu, W.-L. (2006). “Design of TS Fuzzy Model Based on Pareto-coevolution Algorithm”. Control and Decision, Vol. 21, No. 12, pp. 1332-1337.

 Zhou, S.-M., Gan, J.Q. (2006). “Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model”. Studies in Computational Intelligence, Vol. 16, pp. 385-403.

 Zong-Yi X., Yuan-Long H., Zhong-Zhi T. and Li-Min T. (2006). “Construction of Fuzzy Classification System Based on Multi-Objective Genetic Algorithm”. International Conference on Intelligent Systems Design and Applications (ISDA'06).

Year 2005 : 7 citations

 Han M. Fan Y. and Guo W. (2005). “A Modified Neural Network Based on Subtractive Clustering for Bidding System”. Proceedings of the International Conference on Neural Networks and Brain, 2005 - ICNN&B’05.

 Herrera L. J., Pomares H., Rojas I., Valenzuela O. and Prieto A. (2005). “TaSe, a Taylor Series-Based Fuzzy System Model that Combines Interpretability and Accuracy”. Fuzzy Sets and Systems, Vol. 153(3), pp. 403 427.

 Herrera L.J., Pomares H., Rojas I., Guilen A. Awad, M. and Gonzalez, J. (2005). “Interpretable Rule Extraction and Function Approximation from Numerical Input/Output Data Using the Modified Fuzzy TSK Model, TaSe Model”. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNAI, Vol. 3641, pp. 402-411.

 Hong C. M., Huang C. Y. (2005). “A Novel Clustering-based Fuzzy Classifier for Medical Diagnosis”. Proceedings of 2005 CACS Automatic Control Conference, Taiwan.

 Mikut R. Jakel J and Groll L. (2005). “Interpretability issues in data-based learning of fuzzy systems”. Fuzzy Sets and Systems, Vol. 150 (2), pp.179-197.

 Stammitti A., Ledanois J. M. and González-Mendizabal D. (2005). “Gas–Liquid Flow Pattern Maps for Horizontal Pipes by Means of Fuzzy Logic”. Proceeedings of the 4th Mercosur Congress on Process Systems Engineering, Rio de Janeiro, Brazil.

 Xiong S.-W., Niu, X.-X. and Liu, H.-B (2005). “Support Vector Machines Based on Subtractive Clustering”. International Conference on Machine Learning and Cybernetics - ICMLC 2005, pp. 4345-4350.

Year 2004 : 1 citations

 1. Kouxa, Pavel. "Design Optimization of Non-Local Neuro-Fuzzy Models." (Optimizatsiya i sintez nelokal'nyh lingvisticheskih neiro-nechetkih modelei) Inter-universities Scientific and Technical Conference on Modern Information Technologiesï. Moscow, 2004.