Guest Editorial : Evolving Fuzzy Systems : preface to the special section.

It is a well-recognized fact that the theory of fuzzy sets and systems, for the last four decades after the seminal paper by Professor Zadeh [1], has demonstrated its remarkable ability to go beyond conventional information representation. It resulted in a wide range of new formulations of practical problems, such as fuzzy control, fuzzy clustering and classification, fuzzy modeling, and fuzzy optimization [2]. Historically, the design of the fuzzy systems has been initially assumed to be centered on expert knowledge [3]. During the 1990s, a new trend emerged [4], [5] that offered techniques to make use of the experimental data. This data-centered approach can be used to enhance and validate the existing expert knowledge or can also be used to substitute its lack (as is the case with autonomous systems, for example). Neurofuzzy and hybrid learning systems were introduced, where fuzzy representation was integrated into a neural learning architecture to bring linguistic meaning of the learned information [5]. (c) IEEE Pres

Real-time Nonlinear Modeling Of A Twin Rotor Mimo System Using Evolving Neuro-fuzzy Network

This paper presents an evolving neuro-fuzzy network approach (eNFN) to model a twin rotor MIMO system (TRMS) with two degrees of freedom in real-time. The TRMS is a fast, nonlinear, open loop unstable time-varying dynamic system, with cross coupling between the rotors. Modeling and control of TRMS require high sampling rates, typically in the order of milliseconds. Actual laboratory implementation shows that eNFN is fast, effective, and accurately models the TRMS in real-time. The eNFN captures the TRMS system dynamics quickly, and develops precise low cost models from the point of view of time and space complexity. 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Lecture Notes in Computer Science, 6178, pp. 360-369Lughofer, E., Macian, V., Guardiola, C., Klement, E., Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems (2011) Applied Soft Computing, 11 (2), pp. 2487-2500Leite, D., Ballini, R., Costa, P., Gomide, F., Evolving fuzzy granular modeling from nonstationary fuzzy data streams (2012) Evolving Systems, 3, pp. 65-79Rahideh, A., Shaheed, M., Robust model predictive control of a twin rotor MIMO system (2009) Proceedings of IEEE International Conference on the Mechatronics, ICM'09, pp. 1-6Rahideh, A., Shaheed, M., Huijberts, H., Stable adaptive model predictive control for nonlinear systems (2008) Proceedings of the American Control Conference, pp. 1673-1678Rahideh, A., Bajodah, A., Shaheed, M., Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks (2012) Engineering Applications of Artificial Intelligence, 25 (6), pp. 1289-1297Toha, S., Tokhi, M., Inverse model based control for a twin rotor system (2010) Proceedings of the IEEE 9th International Conference on Cybernetic Intelligent Systems, CIS'10, pp. 1-5Silva, A., Caminhas, W., Lemos, A., Gomide, F., A fast learning algorithm for evolving neo-fuzzy neuron (2014) Applied Soft Computing, Part B, 14 (0), pp. 194-209Yamakawa, T., Uchino, E., Miki, T., Kusabagi, H., A neo fuzzy neuron and its applications to system identification and predictions to system behavior (1992) Proceedings of the International Conference on Fuzzy Logic and Neural Networks, 1, pp. 477-484Caminhas, W., Gomide, F., A fast learning algorithm for neofuzzy networks (2000) Proceedings of the Information Processing and Management of Uncertainty in Knowledge Based Systems, IPMU'00, 1 (1), pp. 1784-1790Lemos, A., Caminhas, W., Gomide, F., Fuzzy evolving linear regression trees (2011) Evolving Systems, 2, pp. 1-14Mathworks, I., (2009) Real-time Workshop 7 Users Guide, , Natick, MA, USAKasabov, N., Song, Q., Denfis: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction (2002) IEEE Transactions on Fuzzy Systems, 10 (2), pp. 144-154Angelov, P., Filev, D., An approach to online identification of takagi-sugeno fuzzy models (2004) IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 34 (1), pp. 484-498Angelov, P., Zhou, X., Evolving fuzzy systems from data streams in real-time (2006) Proceedings of the International Symposium on Evolving Fuzzy Systems, pp. 29-3