[Adjrad, M. and Belouchrani, A. (2007). Estimation of multicomponent polynomial-phase signals impinging on a multisensor array using state-space modeling, IEEE Transactions on Signal Processing55(1): 32–45.10.1109/TSP.2006.882055]Search in Google Scholar
[Angelov, P.P., Filev, D.P. (2004). Flexible models with evolving structure, International Journal of Intelligent Systems19(4): 327–340.10.1002/int.10166]Search in Google Scholar
[Babuska, R., Verbruggen, H. (2003). Flexible neuro-fuzzy methods for nonlinear system identification, Annual Reviews in Control27(1): 73–85.10.1016/S1367-5788(03)00009-9]Search in Google Scholar
[Bagarinao, E., Matsuo, K., Nakai, T. and Sato, S. (2003). Estimation of general linear model coefficients for real-time application, NeuroImage19(2): 422–429.10.1016/S1053-8119(03)00081-8]Search in Google Scholar
[Banerjee, A., Arkun, Y., Ogunnaike, B. and Pearson, R. (1997). Estimation of nonlinear systems using linear multiple models, AIChE Journal43(5): 1204–1226.10.1002/aic.690430511]Search in Google Scholar
[Bohlin, T.P. (2006). Practical Grey-Box Process Identification: Theory and Applications, Springer, London.]Search in Google Scholar
[Boukezzoula, R., Galichet, S. and Foulloy, L. (2007). Fuzzy feedback linearizing controller and its equivalence with the fuzzy nonlinear internal model control structure, International Journal of Applied Mathematics and Computer Science17(2): 233–248, DOI: 10.2478/v10006-007-0021-4.10.2478/v10006-007-0021-4]Search in Google Scholar
[Casillas, J., Cordón, O., Herrera, F. and Magdalena, L. (2003). Interpretability improvements to find the balance interpretability-accuracy in fuzzy modeling: An overview, in J. Casillas et al. (Eds.), Interpretability Issues in Fuzzy Modeling, Springer, Berlin/Heidelberg, pp. 3–22.10.1007/978-3-540-37057-4_1]Search in Google Scholar
[Caughey, T.K. (1963). Equivalent linearization techniques, Journal of the Acoustical Society of America35(11): 1706–1711.10.1121/1.1918794]Search in Google Scholar
[Cordón, O. (2011). A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems, International Journal of Approximate Reasoning52(6): 894–913.10.1016/j.ijar.2011.03.004]Search in Google Scholar
[Cordón, O., Herrera, F., Hoffmann, F. and Magdalena, L. (2001). Genetic Fuzzy Systems, World Scientific Publishing Company, Singapore.10.1142/4177]Search in Google Scholar
[Cpałka, K. (2009a). A new method for design and reduction of neuro-fuzzy classification systems, IEEE Transactions on Neural Networks20(4): 701–714.10.1109/TNN.2009.201242519273042]Search in Google Scholar
[Cpałka, K. (2009b). On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification, Nonlinear Analysis A: Theory, Methods and Applications71(12): 1659–1672.10.1016/j.na.2009.02.028]Search in Google Scholar
[Cpałka, K., Łapa, K., Przybył, A. and Zalasiński, M. (2014). A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects, Neuro-computing135: 203–217.10.1016/j.neucom.2013.12.031]Search in Google Scholar
[Cpałka, K., Rebrova, O., Nowicki, R. and Rutkowski, L. (2013). On design of flexible neuro-fuzzy systems for nonlinear modelling, International Journal of General Systems42(6): 706–720.10.1080/03081079.2013.798912]Search in Google Scholar
[Czekalski, P. (2006). Evolution-fuzzy rule based system with parameterized consequences, International Journal of Applied Mathematics and Computer Science16(3): 373–385.]Search in Google Scholar
[DeHaan, D. and Guay, M. (2006). A new real-time perspective on non-linear model predictive control, Journal of Process Control16(6): 615–624.10.1016/j.jprocont.2005.10.002]Search in Google Scholar
[Di Nuovo, A. and Ascia, G. (2013). A fuzzy system index to preserve interpretability in deep tuning of fuzzy rule based classifiers, Journal of Intelligent and Fuzzy Systems25(2): 493–504.10.3233/IFS-120660]Search in Google Scholar
[Eiben, A.E. and Smith, J. (2008). Introduction to Evolutionary Computing, Springer, Berlin/Heidelberg.]Search in Google Scholar
[Fei, X., Lu, C.-C. and Liu, K. (2011). A Bayesian dynamic linear model approach for real-time short-term freeway travel time prediction, Transportation Research C: Emerging Technologies19(6): 1306–1318.10.1016/j.trc.2010.10.005]Search in Google Scholar
[Fogel, D.B. (2006). Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, Vol. 1, John Wiley & Sons, Hoboken, NJ.]Search in Google Scholar
[Fogel, D.B. and Atmar, J.W. (1990). Comparing genetic operators with Gaussian mutations in simulated evolutionary processes using linear systems, Biological Cybernetics63(2): 111–114.10.1007/BF00203032]Search in Google Scholar
[Forst, W. and Hoffmann, D. (2010). Optimization Theory and Practice, Springer, New York, NY.10.1007/978-0-387-78977-4]Search in Google Scholar
[Gabryel, M. and Rutkowski, L. (2006). Evolutionary learning of Mamdani-type neuro-fuzzy systems, in L. Rutkowski et al. (Eds.), Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, Vol. 4029, Springer, Berlin/Heidelberg, pp. 354–359.10.1007/11785231_38]Search in Google Scholar
[Gacto, M., Alcala, R. and Herrera, F. (2011). Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures, Information Sciences181(20): 4340–4360.10.1016/j.ins.2011.02.021]Search in Google Scholar
[Grabowski, P. and Callier, F.M. (2001). Circle criterion and boundary control systems in factor form: Input-output approach, Applied Mathematics and Computer Science11(6): 1387–1403.]Search in Google Scholar
[Háber, R. and Keviczky, L. (1999). Nonlinear System Identification—Input-Output Modeling Approach, Vol. 1: Nonlinear System Parameter Identification, Springer Netherlands, Dordrecht.10.1007/978-94-011-4481-0]Search in Google Scholar
[Homaifar, A. and McCormick, E. (1995). Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms, IEEE Transactions on Fuzzy Systems3(2): 129–139.10.1109/91.388168]Search in Google Scholar
[Horzyk, A. and Tadeusiewicz, R. (2004). Self-optimizing neural networks, Advances in Neural Networks, Springer, Berlin/Heidelberg, pp. 150–155.]Search in Google Scholar
[Huijberts, H., Nijmeijer, H. and Willems, R. (2000). System identification in communication with chaotic systems, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications47(6): 800–808.10.1109/81.852932]Search in Google Scholar
[Ikonen, E. and Najim, K. (2001). Advanced Process Identification and Control, Vol. 9, CRC Press, New York, NY.10.1201/9781482294699]Search in Google Scholar
[Ishibashi, R. and Lucio Nascimento, Jr., C. (2013). GFRBS-PHM: A genetic fuzzy rule-based system for PHM with improved interpretability, IEEE Conference on Prognostics and Health Management, 2013, Gaithersburg, MD, USA, pp. 1–7.]Search in Google Scholar
[Ishibuchi, H. and Yamamoto, T. (2004). Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining, Fuzzy Sets and Systems141(1): 59–88.10.1016/S0165-0114(03)00114-3]Search in Google Scholar
[Jang, I.-S. R. and Sun, C.-T. (1995). Neuro-fuzzy modeling and control, Proceedings of the IEEE83(3): 378–406.10.1109/5.364486]Search in Google Scholar
[Johansen, T.A., Shorten, R. and Murray-Smith, R. (2000). On the interpretation and identification of dynamic Takagi–Sugeno fuzzy models, IEEE Transactions on Fuzzy Systems8(3): 297–313.10.1109/91.855918]Search in Google Scholar
[Johansson, U., Sönströd, C., Norinder, U. and Boström, H. (2011). Trade-off between accuracy and interpretability for predictive in silico modeling, Future Medicinal Chemistry3(6): 647–663.10.4155/fmc.11.2321554073]Search in Google Scholar
[Jordan, A. (2006). Linearization of non-linear state equation, Bulletin of the Polish Academy of Sciences: Technical Sciences54(1): 63–73.]Search in Google Scholar
[Juang, C.-F. and Chen, C.-Y. (2013). Data-driven interval type-2 neural fuzzy system with high learning accuracy and improved model interpretability, IEEE Transactions on Cybernetics43(6): 1781–1795.10.1109/TSMCB.2012.223025324273147]Search in Google Scholar
[Kim, M.-S., Kim, C.-H. and Lee, J.-J. (2006). Evolving compact and interpretable Takagi–Sugeno fuzzy models with a new encoding scheme, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics36(5): 1006–1023.10.1109/TSMCB.2006.872265]Search in Google Scholar
[Kluska, J. (2009). Analytical Methods in Fuzzy Modeling and Control, Springer, Berlin/Heidelberg.10.1007/978-3-540-89927-3]Search in Google Scholar
[Kluska, J. (2015). Selected applications of P1-TS fuzzy rule-based systems, in L. Rutkowski et al. (Eds.), Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, Vol. 9119, Springer, Berlin/Heidelberg, pp. 195–206.10.1007/978-3-319-19324-3_18]Search in Google Scholar
[Kristensen, N.R., Madsen, H. and Jørgensen, S.B. (2004). A method for systematic improvement of stochastic grey-box models, Computers & Chemical Engineering28(8): 1431–1449.10.1016/j.compchemeng.2003.10.003]Search in Google Scholar
[Kroese, D.P., Taimre, T. and Botev, Z.I. (2011). Handbook of Monte Carlo Methods, Vol. 706, John Wiley & Sons, Hoboken, NJ.10.1002/9781118014967]Search in Google Scholar
[Li, C. and Chiang, T.-W. (2012). Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence, International Journal of Applied Mathematics and Computer Science22(4): 787–800, DOI: 10.2478/v10006-012-0058-x.10.2478/v10006-012-0058-x]Search in Google Scholar
[Ljung, L. (2010). Approaches to identification of nonlinear systems, 9th Chinese Control Conference, Beijing, China, pp. 1–5.]Search in Google Scholar
[Łęski, J. (2003). A fuzzy if-then rule-based nonlinear classifier, International Journal of Applied Mathematics and Computer Science13(2): 215–223.10.1016/S0165-0114(02)00372-X]Search in Google Scholar
[Lughofer, E. (2013). On-line assurance of interpretability criteria in evolving fuzzy systems—achievements, new concepts and open issues, Information Sciences251: 22–46.10.1016/j.ins.2013.07.002]Search in Google Scholar
[Malchiodi, D. and Pedrycz, W. (2013). Learning membership functions for fuzzy sets through modified support vector clustering, in F. Masulli et al. (Eds.), Fuzzy Logic and Applications, Springer, Cham, pp. 52–59.10.1007/978-3-319-03200-9_6]Search in Google Scholar
[Medasani, S., Kim, J. and Krishnapuram, R. (1998). An overview of membership function generation techniques for pattern recognition, International Journal of Approximate Reasoning19(3): 391–417.10.1016/S0888-613X(98)10017-8]Search in Google Scholar
[Miller, G.A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information, The Psychological Review63: 81–97.10.1037/h0043158]Search in Google Scholar
[Mrugalski, M. (2014). Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis, Studies in Computational Intelligence, Vol. 510, Springer-Verlag, Berlin/Heidelberg.]Search in Google Scholar
[Murray-Smith, R. and Johansen, T. (1997). Multiple Model Approaches to Nonlinear Modelling and Control, CRC Press, Boca Raton, FL.]Search in Google Scholar
[Nelles, O. (2001). Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models, Springer, Berlin/Heidelberg.]Search in Google Scholar
[Ogata, K. (2004). System Dynamics, Pearson/Prentice Hall, Upper Saddle River, NJ.]Search in Google Scholar
[Patton, R.J., Korbicz, J., Witczak, M. and Uppal, F. (2005). Combined computational intelligence and analytical methods in fault diagnosis, IEE Control Engineering Series70: 349.10.1049/PBCE070E_ch11]Search in Google Scholar
[Pedro, J.O. and Dahunsi, O.A. (2011). Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system, International Journal of Applied Mathematics and Computer Science21(1): 137–147, DOI: 10.2478/v10006-011-0010-5.10.2478/v10006-011-0010-5]Search in Google Scholar
[Przybył, A. and Jelonkiewicz, J. (2003). Genetic algorithm for observer parameters tuning in sensorless induction motor drive, Proceedings of the 6th International Conference on Neural Networks and Soft Computing, Zakopane Poland, pp. 376–381.]Search in Google Scholar
[Puig, V., Witczak, M., Nejjari, F., Quevedo, J. and Korbicz, J. (2007). A GMDH neural network-based approach to passive robust fault detection using a constraint satisfaction backward test, Engineering Applications of Artificial Intelligence20(7): 886–897.10.1016/j.engappai.2006.12.005]Search in Google Scholar
[Quah, K.H. and Quek, C., (2006). FITSK: Online local learning with generic fuzzy input Takagi–Sugeno–Kang fuzzy framework for nonlinear system estimation, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics36(1): 166-178.10.1109/TSMCB.2005.85671516468575]Search in Google Scholar
[Roffel, B. and Betlem, B.H. (2004). Advanced Practical Process Control, Springer, Berlin/Heidelberg.10.1007/978-3-642-18258-7]Search in Google Scholar
[Rüping, S. (2006). Learning Interpretable Models, Ph.D. thesis, Technical University of Dortmund, Dortmund.]Search in Google Scholar
[Rutkowski, L. (2008). Computational Intelligence: Methods and Techniques, Springer, Berlin/Heidelberg.]Search in Google Scholar
[Rutkowski, L. and Cpałka, K. (2005). Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems, IEEE Transactions on Fuzzy Systems13(1): 140–151.10.1109/TFUZZ.2004.836069]Search in Google Scholar
[Salapa, K., Trawińska, A., Roterman, I. and Tadeusiewicz, R. (2014). Speaker identification based on artificial neural networks. Case study: The Polish vowel (a pilot study), Bio-Algorithms and Med-Systems10(2): 91–99.]Search in Google Scholar
[Setnes, M. and Roubos, H. (2000). GA-fuzzy modeling and classification: Complexity and performance, IEEE Transactions on Fuzzy Systems8(5): 509–522.10.1109/91.873575]Search in Google Scholar
[Schröder, D. (Ed.) (2000). Intelligent Observer and Control Design for Nonlinear Systems, Springer, Berlin/Heidelberg.10.1007/978-3-662-04117-8]Search in Google Scholar
[Shill, P., Akhand, M. and Murase, K. (2011). Simultaneous design of membership functions and rule sets for type-2 fuzzy controllers using genetic algorithms, 14th International Conference on Computer and Information Technology, Dhaka, Bangladesh, pp. 554–559.]Search in Google Scholar
[Shukla, P. and Tripathi, S. (2013). Interpretability issues in evolutionary multi-objective fuzzy knowledge base systems, 7th International Conference on Bio-Inspired Computing: Theories and Applications, Madhya Pradesh, India, pp. 473–484.]Search in Google Scholar
[Sivanandam, S. and Deepa, S. (2008). Genetic Algorithm Optimization Problems, Springer, Berlin/Heidelberg.]Search in Google Scholar
[Starczewski, J.T., Bartczuk, Ł., Dziwiński, P. and Marvuglia, A. (2010). Learning methods for type-2 FLS based on FCM, in L. Rutkowski et al. (Eds.), Artificial Intelligence and Soft Computing, Springer, Berlin/Heidelberg, pp. 224–231.10.1007/978-3-642-13208-7_29]Search in Google Scholar
[Tadeusiewicz, R. (2010). Using neural networks for simplified discovery of some psychological phenomena, in L. Rutkowski et al. (Eds.), Artificial Intelligence and Soft Computing, Springer, Berlin/Heidelberg, pp. 104–123.10.1007/978-3-642-13232-2_14]Search in Google Scholar
[Tadeusiewicz, R., Chaki, R. and Chaki, N. (2014). Exploring Neural Networks with C#, CRC Press, Boca Raton, FL.]Search in Google Scholar
[Tadeusiewicz, R. and Figura, I. (2011). Phenomenon of tolerance to damage in artificial neural networks, Computer Methods in Materials Science11(4): 501–513.]Search in Google Scholar
[Tan, Y. (2004). Time-varying time-delay estimation for nonlinear systems using neural networks, International Journal of Applied Mathematics and Computer Science14(1): 63–68.]Search in Google Scholar
[Wang, H., Kwong, S., Jin, Y., Wei, W. and Man, K.-F. (2005). Agent-based evolutionary approach for interpretable rule-based knowledge extraction, IEEE Transactions on Systems, Man, and Cybernetics C: Applications and Reviews35(2): 143–155.10.1109/TSMCC.2004.841910]Search in Google Scholar
[Wang, H., Kwong, S., Jin, Y., Wei, W. and Man, K.-F. (2005). Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction, Fuzzy Sets and Systems149(1): 149–186.10.1016/j.fss.2004.07.013]Search in Google Scholar
[Wilamowski, B.M. (2005). Methods of computational intelligence for nonlinear control systems, ICCAE 2005 International Conference on Control, Automation and System, Gyeonggi-Do, Korea, pp. P1–P8.]Search in Google Scholar
[Witkowska, A. andŚmierzchalski, R. (2012). Designing a ship course controller by applying the adaptive backstepping method, International Journal of Applied Mathematics and Computer Science22(4): 985–997, DOI: 10.2478/v10006-012-0073-y.10.2478/v10006-012-0073-y]Search in Google Scholar
[Wu, C.-J. and Liu, G.-Y. (2000). A genetic approach for simultaneous design of membership functions and fuzzy control rules, Journal of Intelligent and Robotic Systems28(3): 195–211.10.1023/A:1008186427312]Search in Google Scholar
[Xie, Y., Guo, B., Xu, L., Li, J. and Stoica, P. (2006). Multistatic adaptive microwave imaging for early breast cancer detection, IEEE Transactions on Biomedical Engineering53(8): 1647.10.1109/TBME.2006.87805816916099]Search in Google Scholar
[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 Systems159(23): 3091–3131.10.1016/j.fss.2008.05.016]Search in Google Scholar