{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:35:52Z","timestamp":1778823352758,"version":"3.51.4"},"publisher-location":"Cham","reference-count":103,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319679457","type":"print"},{"value":"9783319679464","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,9,23]],"date-time":"2017-09-23T00:00:00Z","timestamp":1506124800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-67946-4_9","type":"book-chapter","created":{"date-parts":[[2017,9,22]],"date-time":"2017-09-22T10:33:58Z","timestamp":1506076438000},"page":"225-264","source":"Crossref","is-referenced-by-count":10,"title":["New Aspects of Interpretability of Fuzzy Systems for Nonlinear Modeling"],"prefix":"10.1007","author":[{"given":"Krystian","family":"\u0141apa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krzysztof","family":"Cpa\u0142ka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leszek","family":"Rutkowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,9,23]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1109\/TFUZZ.2009.2023113","volume":"17","author":"R Alcal\u00e1","year":"2009","unstructured":"Alcal\u00e1, R., Ducange, P., Herrera, F., Lazzerini, B., Marcelloni, F.: A multi-objective evolutionary approach to concurrently learn rule and data base soft linguistic fuzzy rule-based systems. IEEE Trans. Fuzzy Syst. 17, 1106\u20131122 (2009)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Alonso, J.M., Magdalena, L., Cord\u00f3n, O.: Embedding HILK in a three-objective evolutionary algorithm with the aim of modeling highly interpretable fuzzy rule-based classifiers. In: 4th International Workshop on Genetic and Evolving Fuzzy Systems (GEFS2010), pp. 15\u201320 (2010)","DOI":"10.1109\/GEFS.2010.5454165"},{"key":"9_CR3","unstructured":"Alonso, J.M.: Modeling highly interpretable fuzzy systems. Eur. Centre Soft Comput. (2010)"},{"issue":"10","key":"9_CR4","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1007\/s00500-010-0628-5","volume":"15","author":"JM Alonso","year":"2011","unstructured":"Alonso, J.M., Magdalena, L.: HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers. Soft Comput. 15(10), 1959\u20131980 (2011)","journal-title":"Soft Comput."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Althoff, M., Stursberg, O., Buss, M.: Reachability analysis of nonlinear systems with uncertain parameters using conservative linearization. In: Proceedings of the 47th IEEE Conference on Decision and Control, pp. 4042\u20134048 (2008)","DOI":"10.1109\/CDC.2008.4738704"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Amor, N.B., Salem, B., Zied, E.: Naive Bayes vs decision trees in intrusion detection systems. In: Proceedings of the 2004 ACM Symposium on Applied Computing (2004)","DOI":"10.1145\/967900.967989"},{"issue":"4","key":"9_CR7","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1111\/1467-9868.00363","volume":"64","author":"C Andrieu","year":"2002","unstructured":"Andrieu, C., Doucet, A.: Particle filtering for partially observed Gaussian state space models. JR Stat. Soc. B 64(4), 827\u2013836 (2002)","journal-title":"JR Stat. Soc. B"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Bartczuk, \u0141., Przyby\u0142, A., Koprinkova-Hristova, P.: New method for non-linear correction modelling of dynamic objects with genetic programming. In: Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, vol. 9120, pp. 318\u2013329 (2015)","DOI":"10.1007\/978-3-319-19369-4_29"},{"key":"9_CR9","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s00500-008-0360-6","volume":"13","author":"A Botta","year":"2009","unstructured":"Botta, A., Lazzerini, B., Marcelloni, F., Stefanescu, D.C.: Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index. Soft Comput. 13, 437\u2013449 (2009)","journal-title":"Soft Comput."},{"key":"9_CR10","volume-title":"Time Series Analysis: Forecasting and Control","author":"G Box","year":"1970","unstructured":"Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1970)"},{"issue":"4","key":"9_CR11","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1515\/jaiscr-2016-0017","volume":"6","author":"\u00cd Brasileiro","year":"2016","unstructured":"Brasileiro, \u00cd., Santos, I., Soares, A., Rablo, R., Mazullo, F.: Ant colony optimization applied to the problem of choosing the best combination among M combinations of shortest paths in transparent optical networks. J. Artif. Intell. Soft Comput. Res. 6(4), 231\u2013242 (2016)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"9_CR12","unstructured":"Brooks, T.F., Pope, D.S., Marcolini, A.M.: Airfoil self-noise and prediction. Technical report, NASA RP-1218 (1989)"},{"issue":"2","key":"9_CR13","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MVT.2009.932540","volume":"4","author":"K Chen","year":"2009","unstructured":"Chen, K.: Global modeling of different vehicles. IEEE Veh. Technol. Mag. 4(2), 80\u201389 (2009)","journal-title":"IEEE Veh. Technol. Mag."},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Chen, X., Abraham, E., Sankaranarayanan, S.: Flow*: an analyzer for non-linear hybrid systems. In: Proceedings of the 25th International Conference on Computer Aided Verification, vol. 8044, pp. 258\u2013263 (2013)","DOI":"10.1007\/978-3-642-39799-8_18"},{"key":"9_CR15","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/TNN.2009.2012425","volume":"20","author":"K Cpa\u0142ka","year":"2009","unstructured":"Cpa\u0142ka, K.: A new method for design and reduction of neuro-fuzzy classification systems. IEEE Trans. Neural Netw. 20, 701\u2013714 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Cpa\u0142ka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. In: Nonlinear Analysis Series A: Theory, Methods and Applications, vol. 71, pp. 1659\u20131672. Elsevier (2009)","DOI":"10.1016\/j.na.2009.02.028"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Cpa\u0142ka, K.: Design of Interpretable Fuzzy Systems. Springer (2017)","DOI":"10.1007\/978-3-319-52881-6"},{"issue":"6","key":"9_CR18","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1080\/03081079.2013.798912","volume":"42","author":"K Cpa\u0142ka","year":"2013","unstructured":"Cpa\u0142ka, K., Rebrova, O., Nowicki, R., Rutkowski, L.: On design of flexible neuro-fuzzy systems for nonlinear modelling. Int. J. Gen. Syst. 42(6), 706\u2013720 (2013)","journal-title":"Int. J. Gen. Syst."},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Cpa\u0142ka, K., Rutkowski, L.: Flexible Takagi-Sugeno fuzzy systems. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks IJCNN \u201905, vol. 3, pp. 1764\u20131769 (2005)","DOI":"10.1109\/IJCNN.2005.1556147"},{"key":"9_CR20","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/978-3-642-00563-3_6","volume":"59","author":"AK Cyran","year":"2009","unstructured":"Cyran, A.K., Kozielski, S., Peters, F.P., Stanczyk, U., Wakulicz-Deja, A.: Adaptable graphical user interfaces for player-based applications. Adv. Intell. Soft Comput. 59, 69\u201376 (2009)","journal-title":"Adv. Intell. Soft Comput."},{"key":"9_CR21","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"9_CR22","unstructured":"Duch, W., Korbicz, J., Rutkowski, L., Tadeusiewicz, R.: Biocybernetics and biomedical engineering EXIT, Warszawa (2013)"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Duda, P., Hayashi, Y., Jaworski, M.: On the strong convergence of the orthogonal series-type kernel regression neural networks in a non-stationary environment. In: Artificial Intelligence and Soft Computing, vol. 7267, pp. 47\u201354. Springer (2012)","DOI":"10.1007\/978-3-642-29347-4_6"},{"key":"9_CR24","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1515\/jaiscr-2015-0032","volume":"5","author":"AF El-Samak","year":"2015","unstructured":"El-Samak, A.F., Ashour, W.: Optimization of traveling salesman problem using affinity propagation clustering and genetic algorithm. J. Artif. Intell. Soft Comput. Res. 5, 239\u2013246 (2015)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Er, M.J., Duda, P.: On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment. In: International Conference on Parallel Processing and Applied Mathematics. Lecture Notes in Computer Science, vol. 7203, pp. 90\u201398. Springer (2012)","DOI":"10.1007\/978-3-642-31464-3_45"},{"key":"9_CR26","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/91.873582","volume":"8","author":"J Espinosa","year":"2000","unstructured":"Espinosa, J., Vandewalle, J.: Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm. IEEE Trans. Fuzzy Syst. 8, 591\u2013600 (2000)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"3","key":"9_CR27","doi-asserted-by":"crossref","first-page":"33","DOI":"10.5121\/ijfls.2012.2303","volume":"2","author":"F Farahbod","year":"2012","unstructured":"Farahbod, F., Eftekhari, M.: Comparsion of different T-norm operators in classification problems. Int. J. Fuzzy Logic Syst. 2(3), 33\u201341 (2012)","journal-title":"Int. J. Fuzzy Logic Syst."},{"key":"9_CR28","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TBME.2007.895109","volume":"54","author":"P Fazendeiro","year":"2007","unstructured":"Fazendeiro, P., de Oliveira, J.V., Pedrycz, W.: A multiobjective design of a patient and anaesthetist-friendly neuromuscular blockade controller. IEEE Trans. Biomed. Eng. 54, 1667\u20131678 (2007)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"9_CR29","volume-title":"Computer Models in Genetics","author":"A Fraser","year":"1970","unstructured":"Fraser, A., Burnell, D.: Computer Models in Genetics. McGraw-Hill, New York (1970)"},{"key":"9_CR30","unstructured":"Gabryel, M., Cpa\u0142ka, K., Rutkowski, L.: Evolutionary strategies for learning of neuro-fuzzy systems. In: Proceedings of the I Workshop on Genetic Fuzzy Systems, Granada, vol. 119, p. 123 (2005)"},{"issue":"3","key":"9_CR31","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/TFUZZ.2010.2041008","volume":"18","author":"MJ Gacto","year":"2010","unstructured":"Gacto, M.J., Alcal\u00e1, R., Herrera, F.: Integration of an index to preserve the semantic interpretability in the multi-objective evolutionary rule selection and tuning of linguistic fuzzy systems. IEEE Trans. Fuzzy Syst. 18(3), 515\u2013531 (2010)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"20","key":"9_CR32","doi-asserted-by":"crossref","first-page":"4340","DOI":"10.1016\/j.ins.2011.02.021","volume":"181","author":"MJ Gacto","year":"2011","unstructured":"Gacto, M.J., Alcal\u00e1, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181(20), 4340\u20134360 (2011)","journal-title":"Inf. Sci."},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Gorzalczany, M.B., Rudzinski, F.: Accuracy vs. interpretability of fuzzy rule-based classifiers: an evolutionary approach. In: Proceedings of the 2012 International Conference on Swarm and Evolutionary Computation SIDE\u201912, pp. 222\u2013230 (2012)","DOI":"10.1007\/978-3-642-29353-5_26"},{"issue":"3","key":"9_CR34","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1109\/TFUZZ.2004.825979","volume":"12","author":"S Guillaume","year":"2004","unstructured":"Guillaume, S., Charnomordic, B.: Generating an interpretable family of fuzzy partitions from data. IEEE Trans. Fuzzy Syst. 12(3), 324\u2013335 (2004)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Ibrahim, S.S., Bamatraf, M.A.: Interpretation trained neural networks based on genetic algorithms. Int. J. Artif. Intell. Appl. (IJAIA) 4(1), 13\u201322 (2013)","DOI":"10.5121\/ijaia.2013.4102"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Icke, I., Rosenberg, A.: Multi-objective genetic programming for visual analytics. In: Silva, S., et al. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 322\u2013334 (2011)","DOI":"10.1007\/978-3-642-20407-4_28"},{"key":"9_CR37","unstructured":"Ishibuchi, H., Nakashima, T., Murata, T.: Comparsion of the Michigan and Pittsburgh approaches to the design of fuzzy classification systems. Electron. Commun. Jpn. Part 3 80(12), 379\u2013387 (1997)"},{"key":"9_CR38","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/3477.790443","volume":"29","author":"H Ishibuchi","year":"1999","unstructured":"Ishibuchi, H., Nakashima, T., Murata, T.: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans. SMC B Cybern. 29, 601\u2013618 (1999)","journal-title":"IEEE Trans. SMC B Cybern."},{"issue":"4","key":"9_CR39","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1109\/TFUZZ.2004.841738","volume":"13","author":"H Ishibuchi","year":"2005","unstructured":"Ishibuchi, H.: Rule weight specification in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 13(4), 428\u2013436 (2005)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"9_CR40","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.ijar.2006.01.004","volume":"44","author":"H Ishibuchi","year":"2007","unstructured":"Ishibuchi, H., Nojima, Y.: Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. Int. J. Approximate Reasoning 44, 4\u201331 (2007)","journal-title":"Int. J. Approximate Reasoning"},{"key":"9_CR41","doi-asserted-by":"crossref","unstructured":"Jaworski, M., Er, M.J., Pietruczuk, L.: On the application of the Parzen-type kernel regression neural network and order statistics for learning in a non-stationary environment. In: International Conference on Artificial Intelligence and Soft Computing. Lecture Notes in Artificial Intelligence, vol. 7267, pp. 90\u201398. Springer (2012)","DOI":"10.1007\/978-3-642-29347-4_11"},{"key":"9_CR42","unstructured":"Kacprzyk, J.: Studies in Computational Intelligence, vol. 143 (2008)"},{"issue":"4","key":"9_CR43","doi-asserted-by":"crossref","first-page":"897","DOI":"10.2478\/v10006-012-0066-x","volume":"22","author":"T Kaczorek","year":"2012","unstructured":"Kaczorek, T.: A modified state variable diagram method for determination of positive realizations of linear continous-time systems with delays. Int. J. Appl. Math. Comput. Sci. 22(4), 897\u2013905 (2012)","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"9_CR44","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.asoc.2013.10.014","volume":"15","author":"S Kar","year":"2014","unstructured":"Kar, S., Das, S., Ghosh, P.K.: Applications of neuro-fuzzy systems: a brief review and future outline. Appl. Soft Comput. 15, 243\u2013259 (2014)","journal-title":"Appl. Soft Comput."},{"key":"9_CR45","unstructured":"Kamyar, M.: Takagi-Sugeno fuzzy modeling for process control industrial automation. In: Robotics and Artificial Intelligence (EEE8005), School of Electrical, Electronic and Computer Engineering (2008)"},{"key":"9_CR46","unstructured":"Kenesei, T., Abonyi, J.: Interpretable support vector machines in regression and classification-application in process engineering. Hung. J. Ind. Chem. 35, 101\u2013108 (2007)"},{"key":"9_CR47","doi-asserted-by":"crossref","unstructured":"Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic Publishers (2000)","DOI":"10.1007\/978-94-015-9540-7"},{"issue":"2","key":"9_CR48","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0165-0114(97)00337-0","volume":"108","author":"WV Leekwijck","year":"1999","unstructured":"Leekwijck, W.V., Kerre, E.E.: Defuzzification: criteria and classification. Fuzzy Sets Syst. 108(2), 159\u2013178 (1999)","journal-title":"Fuzzy Sets Syst."},{"issue":"2","key":"9_CR49","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1515\/jaiscr-2016-0009","volume":"6","author":"M Leon","year":"2016","unstructured":"Leon, M., Xiong, N.: Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters. J. Artif. Intell. Soft Comput. Res. 6(2), 103\u2013118 (2016)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"9_CR50","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1162\/neco.2007.19.6.1656","volume":"19","author":"F Liu","year":"2007","unstructured":"Liu, F., Quek, C., Ng, G.S.: A novel generic hebbian ordering-based fuzzy rule base reduction approach to Mamdani neuro-fuzzy system. Neural Comput. 19, 1656\u20131680 (2007)","journal-title":"Neural Comput."},{"issue":"1","key":"9_CR51","first-page":"14","volume":"1","author":"W-Y Loh","year":"2011","unstructured":"Loh, W.-Y.: Classification and regression trees. Wiley Interdisc. Rev.: Data Min. Knowl. Discovery 1(1), 14\u201323 (2011)","journal-title":"Wiley Interdisc. Rev.: Data Min. Knowl. Discovery"},{"key":"9_CR52","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/978-3-319-07173-2_20","volume":"8467","author":"K \u0141apa","year":"2014","unstructured":"\u0141apa, K., Cpa\u0142ka, K., Wang, L.: New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability. Lect. Notes Comput. Sci. 8467, 217\u2013232 (2014)","journal-title":"Lect. Notes Comput. Sci."},{"key":"9_CR53","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/978-3-319-19369-4_23","volume":"9120","author":"K \u0141apa","year":"2015","unstructured":"\u0141apa, K., Szczypta, J., Venkatesan, R.: Aspects of structure and parameters selection of control systems using selected multi-population algorithms. Lect. Notes Comput. Sci. 9120, 247\u2013260 (2015)","journal-title":"Lect. Notes Comput. Sci."},{"key":"9_CR54","doi-asserted-by":"crossref","unstructured":"Marquez, A.A, Marquez, F.A., Peregrin, A.: A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification. IEEE Int. Conf. Fuzzy Syst. 1\u20137 (2010)","DOI":"10.1109\/FUZZY.2010.5584294"},{"key":"9_CR55","unstructured":"Mehran, K.: Takagi-Sugeno fuzzy modeling for process control. In: Industrial Automation, Robotics and Artificial Intelligence (EEE8005) (2008)"},{"key":"9_CR56","unstructured":"Mencar, C., Castellano, G., Fanelli, A.M.: Some fundamental interpretability issues in fuzzy modeling. In: Proceedings of the Joint 4th Conference of the European Society for Fuzzy Logic and Technology, pp. 100\u2013105 (2005)"},{"key":"9_CR57","doi-asserted-by":"crossref","unstructured":"Mencar, C., Castellano, G., Fanelli, A.M.: On the role of interpretability in fuzzy data mining. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 521\u2013537 (2007)","DOI":"10.1142\/S0218488507004856"},{"issue":"4","key":"9_CR58","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.ijar.2010.11.007","volume":"52","author":"C Mencar","year":"2011","unstructured":"Mencar, C., Castiello, C., Cannone, R., Fanelli, A.M.: Interpretability assessment of fuzzy knowledge bases: a cointension based approach. Int. J. Approximate Reasoning 52(4), 501\u2013518 (2011)","journal-title":"Int. J. Approximate Reasoning"},{"key":"9_CR59","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1037\/h0043158","volume":"63","author":"GA Miller","year":"1956","unstructured":"Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81\u201397 (1956)","journal-title":"Psychol. Rev."},{"issue":"4","key":"9_CR60","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1515\/jaiscr-2015-0035","volume":"5","author":"H Miyajima","year":"2015","unstructured":"Miyajima, H., Shigei, N., Miyajima, H.: Performance comparison of hybrid electromagnetism-like mechanism algorithms with descent method. J. Artif. Intell. Soft Comput. Res. 5(4), 271\u2013282 (2015)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"9_CR61","unstructured":"Musa, A.A.H., Muawia, M.A.: Analysis of the DC motor speed control using state variable transition matrix. Int. J. Sci. Res. (IJSR) 2758\u20132763 (2012)"},{"issue":"2","key":"9_CR62","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1515\/jaiscr-2016-0006","volume":"6","author":"KP Nguyen","year":"2016","unstructured":"Nguyen, K.P., Fujita, G., Dieu, V.N.: Cuckoo search algorithm for optimal placement and sizing of static VAR compensator in large-scale power systems. J. Artif. Intell. Soft Comput. Res. 6(2), 59\u201368 (2016)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"1","key":"9_CR63","doi-asserted-by":"crossref","first-page":"225","DOI":"10.2478\/v10006-012-0017-6","volume":"22","author":"K Patan","year":"2012","unstructured":"Patan, K., Korbicz, J.: Nonlinear model predictive control of a boiler unit: a fault tolerant control study. Int. J. Appl. Math. Comput. Sci. 22(1), 225\u2013237 (2012)","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"9_CR64","doi-asserted-by":"crossref","unstructured":"Pietruczuk, L., Duda, P., Jaworski, M.: Adaptation of decision trees for handling concept drift. In: International Conference on Artificial Intelligence and Soft Computing. Lecture Notes in Artificial Intelligence, vol. 7894, pp. 459\u2013473. Springer (2013)","DOI":"10.1007\/978-3-642-38658-9_41"},{"key":"9_CR65","doi-asserted-by":"crossref","unstructured":"Przyby\u0142, A., Cpa\u0142ka, K.: A new method to construct of interpretable models of dynamic systems. Lect. Notes Artif. Intell. 697\u2013705 (2012)","DOI":"10.1007\/978-3-642-29350-4_82"},{"issue":"1","key":"9_CR66","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/TFUZZ.2009.2038712","volume":"18","author":"P Pulkkinen","year":"2010","unstructured":"Pulkkinen, P., Koivisto, H.: A dynamically constrained multiobjective genetic fuzzy system for regression problems. IEEE Trans. Fuzzy Syst. 18(1), 161\u2013177 (2010)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"9_CR67","doi-asserted-by":"crossref","unstructured":"Riid, A., Rustern, E.: Interpretability improvement of fuzzy systems: reducing the number of unique singletons in zeroth order Takagi-Sugeno systems. IEEE Int. Conf. Fuzzy Syst. 1\u20136 (2010)","DOI":"10.1109\/FUZZY.2010.5584515"},{"key":"9_CR68","doi-asserted-by":"crossref","unstructured":"Riid, A., Rustern, E.: Interpretability, interpolation and rule weights in linguistic fuzzy modeling. In: Petrosino, A., et al. (eds.) WILF 2011. LNAI, vol. 6857, pp. 91\u201398 (2011)","DOI":"10.1007\/978-3-642-23713-3_12"},{"issue":"1","key":"9_CR69","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.ins.2012.12.048","volume":"257","author":"A Riid","year":"2014","unstructured":"Riid, A., Rustern, E.: Adaptability, interpretability and rule weights in fuzzy rule-based systems. Inf. Sci. 257(1), 301\u2013312 (2014)","journal-title":"Inf. Sci."},{"key":"9_CR70","unstructured":"Rosfariedzah, R., Nagarajan, R., Rahim, M.: Fuzzy variable structure control with reduced-order observer for micro satellite stabilization in space. In: Proceedings of the International Conference on Man-Machine Systems (ICoMMS), pp. 11\u201313 (2009)"},{"key":"9_CR71","unstructured":"Rutkowski, L.: Flexible Neuro-Fuzzy Systems. Kluwer Academic Publishers (2004)"},{"key":"9_CR72","doi-asserted-by":"crossref","unstructured":"Rutkowski, L.: Computational Intelligence. Springer (2008)","DOI":"10.1007\/978-3-540-76288-1"},{"key":"9_CR73","doi-asserted-by":"crossref","unstructured":"Rutkowski, L., Cpa\u0142ka, K.: A general approach to neuro-fuzzy systems. In: The 10th IEEE International Conference on Fuzzy Systems, 2001, Melbourne, pp. 1428\u20131431 (2001)","DOI":"10.1109\/FUZZ.2001.1008927"},{"key":"9_CR74","unstructured":"Rutkowski, L., Cpa\u0142ka, K.: Compromise approach to neuro-fuzzy systems. In: 2nd Euro-International Symposium on Computation Intelligence, vol. 76, pp. 85\u201390, Kosice, Slovakia, 16\u201319 June 2002"},{"issue":"2","key":"9_CR75","first-page":"297","volume":"31","author":"L Rutkowski","year":"2002","unstructured":"Rutkowski, L., Cpa\u0142ka, K.: A neuro-fuzzy controller with a compromise fuzzy reasoning. Control Cybern. 31(2), 297\u2013308 (2002)","journal-title":"Control Cybern."},{"key":"9_CR76","doi-asserted-by":"crossref","unstructured":"Rutkowski, L., Cpa\u0142ka, K.: Neuro-fuzzy systems derived from quasi-triangular norms. In: Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1031\u20131036, Budapest, 26\u201329 July 2004","DOI":"10.1109\/FUZZY.2004.1375551"},{"key":"9_CR77","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/TFUZZ.2004.836069","volume":"13","author":"L Rutkowski","year":"2005","unstructured":"Rutkowski, L., Cpa\u0142ka, K.: Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems. IEEE Trans. Fuzzy Syst. 13, 140\u2013151 (2005)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"2003","key":"9_CR78","first-page":"554","volume":"14","author":"L Rutkowski","year":"2013","unstructured":"Rutkowski, L., Cpa\u0142ka, K.: Flexible neuro fuzzy systems. IEEE Trans. Neural Netw. 14(2003), 554\u2013574 (2013)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"2","key":"9_CR79","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1109\/TIE.2011.2161652","volume":"59","author":"L Rutkowski","year":"2012","unstructured":"Rutkowski, L., Przyby\u0142, A., Cpa\u0142ka, K.: Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. IEEE Trans. Ind. Electron. 59(2), 1238\u20131247 (2012)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"9_CR80","doi-asserted-by":"crossref","unstructured":"Rutkowski, L., Przyby\u0142, A., Cpa\u0142ka, K., Er, M.J.: Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. Lect. Notes Artif. Intell. 114, 645\u2013650 (2010)","DOI":"10.1007\/978-3-642-13232-2_79"},{"key":"9_CR81","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, G., Jim\u00e9nez, F., S\u00e1nchez, J.M., Alcaraz, J.M.: A multi-objective neuro-evolutionary algorithm to obtain interpretable fuzzy models. In: Current Topics in Artificial Intelligence. Lecture Notes in Computer Science, vol. 5988, pp. 51\u201360 (2010)","DOI":"10.1007\/978-3-642-14264-2_6"},{"key":"9_CR82","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/978-3-642-13208-7_27","volume":"6113","author":"R Scherer","year":"2010","unstructured":"Scherer, R.: Neuro-fuzzy systems with relation matrix. Artif. Intell. Soft Comput. 6113, 210\u2013215 (2010)","journal-title":"Artif. Intell. Soft Comput."},{"key":"9_CR83","doi-asserted-by":"crossref","first-page":"256","DOI":"10.3390\/info3030256","volume":"3","author":"PK Shukla","year":"2012","unstructured":"Shukla, P.K., Tripathi, S.P.: A review on the interpretability-accuracy trade-off in evolutionary multi-objective fuzzy systems (EMOFS). Information 3, 256\u2013277 (2012)","journal-title":"Information"},{"key":"9_CR84","unstructured":"Shukla, P.K., Tripathi, S.P.: Handling high dimensionality and interpretability-accuracy trade-off issues in evolutionary multiobjective fuzzy classifiers. Int. J. Sci. Eng. Res. 5(6), 665\u2013671 (2014)"},{"key":"9_CR85","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/2195-5468-2-4","volume":"2","author":"PK Shukla","year":"2014","unstructured":"Shukla, P.K., Tripathi, S.P.: A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms. J. Uncertainty Anal. Appl. 2, 4 (2014)","journal-title":"J. Uncertainty Anal. Appl."},{"issue":"2","key":"9_CR86","doi-asserted-by":"crossref","first-page":"337","DOI":"10.2478\/v10006-010-0025-3","volume":"20","author":"K Siminski","year":"2010","unstructured":"Siminski, K.: Rule weights in a neuro-fuzzy system with a hierarchical domain partition. Int. J. Appl. Math. Comput. Sci. 20(2), 337\u2013347 (2010)","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"9_CR87","doi-asserted-by":"crossref","unstructured":"Singh, L., Kumar, S., Paul, S.: Automatic simultaneous architecture and parameter search in fuzzy neural network learning using novel variable length crossover differential evolution. In: IEEE International Conference on Fuzzy Systems, pp. 1795\u20131802 (2008)","DOI":"10.1109\/FUZZY.2008.4630614"},{"issue":"4","key":"9_CR88","first-page":"193","volume":"10","author":"R Tadeusiewicz","year":"2010","unstructured":"Tadeusiewicz, R.: Place and role of intelligent systems in computer science. Comput. Methods Mater. Sci. 10(4), 193\u2013206 (2010)","journal-title":"Comput. Methods Mater. Sci."},{"key":"9_CR89","doi-asserted-by":"crossref","unstructured":"Tan, Y., Shi, Y., Tan, K.C.: Fireworks algorithm for optimization. In: ICSI 2010, Part I. LNCS, vol. 6145, pp. 355\u2013364 (2010)","DOI":"10.1007\/978-3-642-13495-1_44"},{"key":"9_CR90","unstructured":"Tan, C.: More than Accuracy: Interpretability. @MLDG 08\/15\/2013. https:\/\/chenhaot.com\/pubs\/mldg-interpretability.pdf (2013)"},{"key":"9_CR91","doi-asserted-by":"crossref","unstructured":"Tikk, D., Gedeon, T., Wong, K.: A feature ranking algorithm for fuzzy modeling problems. In: Interpretability Issues in Fuzzy Modeling, pp. 176\u2013192. Springer (2003)","DOI":"10.1007\/978-3-540-37057-4_8"},{"key":"9_CR92","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.enbuild.2012.03.003","volume":"49","author":"A Tsanas","year":"2012","unstructured":"Tsanas, A., Xifara, A.: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build. 49, 560\u2013567 (2012)","journal-title":"Energy Build."},{"key":"9_CR93","doi-asserted-by":"crossref","unstructured":"Vanhoucke, V., Silipo, R.: Interpretability in multidimensional classification. In: Interpretability Issues in Fuzzy Modeling, pp. 193\u2013217. Springer (2003)","DOI":"10.1007\/978-3-540-37057-4_9"},{"key":"9_CR94","unstructured":"Viharos, Z.J., Kis, K.B.: Survey on neuro-fuzzy systems and their applications in technical diagnostics. In: 13th IMEKO TC10 Workshop on Technical Diagnostics Advanced Measurement Tools in Technical Diagnostics for Systems\u2019 Reliability and Safety (2014)"},{"issue":"1","key":"9_CR95","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.fss.2004.07.013","volume":"149","author":"H Wang","year":"2005","unstructured":"Wang, H., Kwong, S., Jin, Y., Wei, W., Man, K.F.: Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets Syst. 149(1), 149\u2013186 (2005)","journal-title":"Fuzzy Sets Syst."},{"key":"9_CR96","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1515\/jaiscr-2016-0015","volume":"6","author":"CH Yang","year":"2016","unstructured":"Yang, C.H., Moi, S.H., Lin, Y.D., Chuang, L.Y.: Genetic algorithm combined with a local search method for identifying susceptibility genes. J. Artif. Intell. Soft Comput. Res. 6, 203\u2013212 (2016)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"6","key":"9_CR97","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.cemconcomp.2007.02.001","volume":"29","author":"IC Yeh","year":"2007","unstructured":"Yeh, I.C.: Modeling slump flow of concrete using second-order regressions and artificial neural networks. Cement Concr. Compos. 29(6), 474\u2013480 (2007)","journal-title":"Cement Concr. Compos."},{"key":"9_CR98","doi-asserted-by":"crossref","unstructured":"Yin, Z., O\u2019Sullivan C, Brabazon A.: An analysis of the performance of genetic programming for realised volatility forecas. J. Artif. Intell. Soft Computing Res. 6, 155\u2013172 (2016)","DOI":"10.1515\/jaiscr-2016-0012"},{"key":"9_CR99","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/978-3-319-28567-2_12","volume":"432","author":"M Zalasi\u0144ski","year":"2016","unstructured":"Zalasi\u0144ski, M.: New algorithm for on-line signature verification using characteristic global features. Adv. Intell. Syst. Comput. 432, 137\u2013146 (2016)","journal-title":"Adv. Intell. Syst. Comput."},{"key":"9_CR100","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/978-3-319-28567-2_13","volume":"432","author":"M Zalasi\u0144ski","year":"2016","unstructured":"Zalasi\u0144ski, M., Cpa\u0142ka, K.: New algorithm for on-line signature verification using characteristic hybrid partitions. Adv. Intell. Syst. Comput. 432, 147\u2013157 (2016)","journal-title":"Adv. Intell. Syst. Comput."},{"key":"9_CR101","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/978-3-319-39384-1_20","volume":"9693","author":"M Zalasi\u0144ski","year":"2016","unstructured":"Zalasi\u0144ski, M., Cpa\u0142ka, K., Hayashi, Y.: A new approach to the dynamic signature verification aimed at minimizing the number of global features. Lect. Notes Comput. Sci. 9693, 218\u2013231 (2016)","journal-title":"Lect. Notes Comput. Sci."},{"key":"9_CR102","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/978-3-319-39384-1_21","volume":"9693","author":"M Zalasi\u0144ski","year":"2016","unstructured":"Zalasi\u0144ski, M., Cpa\u0142ka, K., Rakus-Andersson, E.: An idea of the dynamic signature verification based on a hybrid approach. Lect. Notes Comput. Sci. 9693, 232\u2013246 (2016)","journal-title":"Lect. Notes Comput. Sci."},{"key":"9_CR103","unstructured":"\u017burada, J.M.: Introduction to Artificial Neural Systems. Jaico Publishing House (2005)"}],"container-title":["Studies in Computational Intelligence","Advances in Data Analysis with Computational Intelligence Methods"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-67946-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T03:45:43Z","timestamp":1693021543000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-67946-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,23]]},"ISBN":["9783319679457","9783319679464"],"references-count":103,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-67946-4_9","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"value":"1860-949X","type":"print"},{"value":"1860-9503","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,23]]}}}