RECURRENT NEURO FUZZY AND FUZZY NEURAL HYBRID NETWORKS: A REVIEW

REFERENCES

• Bhat , N. V. ; Minderman , P. A. ; McAvoy , T. J. ; Wang , N. S. Modeling chemical process systems via neural computation . IEEE Control Syst. Mag. 1990 , 10 ( 3 ), 24 – 30 .
   Google Scholar
• Qureshia , M. F. ; Gabel , J. K. ; Khande , C. S. ; Bharti , I. C. Application of hybrid system control method for real-time power system stabilization . Fuzzy Sets Syst. 2007 , 158 ( 24 ), 2687 – 2705 .
   Google Scholar
• Chen , Y. C. ; Teng , C. C. A model reference control structure using a fuzzy neural network . Fuzzy Sets Syst. 1995 , 73 ( 3 ), 291 – 312 .
   Google Scholar
• Wang , L. X. A Course in Fuzzy Systems and Control ; Prentice-Hall : Englewood Cliffs , NJ , 1997 .
   Google Scholar
• Kiguchi , K. ; Fukuda , T. Intelligent position/force controller for industrial robot manipulators—application of fuzzy neural networks . IEEE Trans. Ind. Electron. 1997 , 44 ( 6 ), 753 – 761 .
   Web of Science ®Google Scholar
• Farag , W. A. ; Quintana , V. H. ; Lambert Torres , G. A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems . IEEE Trans. Neural Networks. 1998 , 9 ( 5 ), 756 – 767 .
   PubMedGoogle Scholar
• Sun , W. ; Wang , Y. A recurrent fuzzy neural network based adaptive control and its application on robotic tracking control . Neural Information Processing—Letters and Review.s 2004 , 5 ( 1 ), 19 – 26 .
   Google Scholar
• Brown , M. ; Harris , C. J. Neuro Fuzzy Adaptive Modeling and Control ; Prentice-Hall : Englewood Cliffs , NJ , 1994 .
   Google Scholar
• Harris , C. J. ; Brown , M. ; Bossley , K. M. ; Mills , D. J. ; Ming , F. Advances in neuro fuzzy algorithms for real-time modeling and control . Eng. Appl. Artif. Intell. 1996 , 9 ( 1 ), 1 – 16 .
   Google Scholar
• Horikawa , S. ; Furuhashi , T. ; Uchikawa , Y. On fuzzy modeling using fuzzy neural networks with the backpropagation algorithm . IEEE Trans. Neural Networks. 1992 , 3 ( 5 ), 801 – 806 .
   PubMed Web of Science ®Google Scholar
• Jang , J. S. R. Self-learning fuzzy controllers based on temporal back propagation . IEEE Trans. Neural Networks. 1992 , 3 ( 5 ), 714 – 723 .
   PubMed Web of Science ®Google Scholar
• Jang , J. S. R. ; Sun , C. T. Neuro-fuzzy modeling and control . Proc. IEEE. 1995 , 83 ( 3 ), 378 – 406 .
   Web of Science ®Google Scholar
• Jang , J. S. R. ; Sun , C. T. ; Mizutani , E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence ; Prentice-Hall : Englewood Cliffs , NJ , 1997 .
   Google Scholar
• Nie , J. ; Linkens , D. A. Learning control using fuzzified self organizing radial basis function networks . IEEE Trans. Fuzzy Syst. 1993 , 1 , 280 – 287 .
   Google Scholar
• Wang , L. X. Adaptive Fuzzy Systems and Control: Design and Stability Analysis ; Prentice-Hall : Englewood Cliffs , NJ , 1994 .
   Google Scholar
• Zhang , J. ; Morris , A. J. On-line process fault diagnosis using fuzzy neural networks . Intell. Syst. Eng. 1994 , 3 ( 1 ), 37 – 47 .
   Google Scholar
• Zhang , J. ; Morris , A. J. Fuzzy neural networks for nonlinear systems modeling . Proc. Inst. Elect. Eng. Contr. Theory Appl. 1995 , 142 ( 6 ), 551 – 561 .
   Google Scholar
• Castro , J. R. ; Castillo , O. ; Melin , P. ; Diaz , A. R. A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks . Inf. Sci. 2009 , 179 ( 13 ), 2175 – 2193 .
   Google Scholar
• Aguilar , L. ; Melin , P. ; Castillo , O. Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach . Appl. Soft Comput. 2003 , 3 ( 3 ), 209 – 219 .
   Google Scholar
• Moustakidis , S. P. ; Rovithakis , G. A. ; Theocharis , J. B. An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems . Automatica. 2008 , 44 ( 5 ), 1418 – 1425 .
   Google Scholar
• Lin , F. J. ; Hwang , W. J. ; Wai , R. J. A supervisory fuzzy neural network control system for tracking periodic inputs . IEEE Trans. Fuzzy Syst. 1999 , 7 ( 1 ), 41 – 52 .
   Google Scholar
• Melin , P. ; Castillo , O. Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory . Appl. Soft Comput. 2003 , 3 ( 4 ), 353 – 362 .
   Google Scholar
• Jang , J. S. R. ANFIS: Adaptive-network-based fuzzy inference system . IEEE Trans. Syst. Man Cybern. 1993 , 23 ( 3 ), 665 – 685 .
   Web of Science ®Google Scholar
• Juang , C. F. An automatic building approach to special Takagi–Sugeno fuzzy network for unknown plant modeling and stable control. Asian J. Control. 2003, 5(2), 176–186.
   Google Scholar
• Juang , C. F. ; Lin , C. T. An on-line self-constructing neural fuzzy inference network and its applications . IEEE Trans. Fuzzy Syst. 1998 , 6 ( 1 ), 12 – 32 .
   Web of Science ®Google Scholar
• Tung , W. L. ; Quek , C. GenSo FNN 2002: A generic self-organizing fuzzy neural network . IEEE Trans. Neural Networks. 2002 , 13 ( 5 ), 1075 – 1086 .
   Google Scholar
• Subathra , B. ; Raja Rao , N. ; Radhakrishnan , T. K. Design of a TRFN controller for a nonlinear multivariable process . Instrum. Sci. Technol. 2009 , 37 , 660 – 675 .
   Web of Science ®Google Scholar
• Juang , C. F. A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms . IEEE Trans. Fuzzy Syst. 2002 , 10 ( 2 ), 155 – 170 .
   Google Scholar
• Lin , C. J. ; Chin , C. C. Prediction and identification using wavelet-based recurrent fuzzy neural networks . IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2004 , 34 ( 5 ), 2144 – 2154 .
   PubMed Web of Science ®Google Scholar
• Mastorocostas , P. A. ; Theocharis , J. B. A recurrent fuzzy-neural model for dynamic system identification . IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2002 , 32 ( 2 ), 176 – 190 .
   PubMed Web of Science ®Google Scholar
• Surmann , H. ; Maniadakis , M. Learning feed forward and recurrent fuzzy systems: A genetic approach . J. Syst. Archit. 2007 , 47 ( 7 ), 649 – 662 .
   Google Scholar
• Wang , Y. C. ; Chien , C. J. ; Teng , C. C. Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network . IEEE Trans. Syst. Man Cybern. B: Cybern. 2004 , 34 ( 3 ), 1348 – 1359 .
   PubMed Web of Science ®Google Scholar
• Zhang , J. ; Morris , A. J. Recurrent neuro-fuzzy networks for nonlinear process modeling . IEEE Trans. Neural Networks. 1999 , 10 ( 2 ), 313 – 326 .
   PubMed Web of Science ®Google Scholar
• Babuska , R. ; Verbruggen , H. Neuro-fuzzy methods for nonlinear system identification . Ann. Rev. Control. 2003 , 27 ( 1 ), 73 – 85 .
   Google Scholar
• Lin , J. H. ; Isik , C. A maximum entropy radial basis function network based neuro-fuzzy controller. In Proceedings of 5th IEEE International Conference on Fuzzy Systems, Sept. 8–11, New Orleans, LA, USA, 1996.
   Google Scholar
• Ku , C. C. ; Lee , K. Y. Diagonal recurrent neural networks for dynamic systems control . IEEE Trans. Neural Networks. 1995 , 6 ( 1 ), 144 – 156 .
   PubMed Web of Science ®Google Scholar
• Lee , C. H. ; Teng , C. C. Identification and control of dynamic systems using recurrent fuzzy neural networks . IEEE Trans. Fuzzy Syst. 2000 , 8 ( 4 ), 349 – 366 .
   Web of Science ®Google Scholar
• Lin , C. J. ; Chin , C. C. Recurrent wavelet-based neuro fuzzy networks for dynamic system identification . Math. Comput. Model. 2005 , 41 , 227 – 239 .
   Google Scholar
• Lin , F. J. ; Wai , R. J.; Hong , C. M. Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs . IEEE Trans. Neural Networks. 2001 , 12 ( 1 ), 68 – 90 .
   Google Scholar
• Isik , C. ; Farrokhi , M. Recurrent neuro-fuzzy systems. Presented at the Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS ‘97, September 21–24, 1997, Syracuse: NY, USA.
   Google Scholar
• Juang , C. F. ; Ku , K. C. A recurrent fuzzy network for fuzzy temporal sequence processing and gesture recognition . IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2005 , 35 ( 4 ), 646 – 657 .
   Google Scholar
• Lin , C. J. ; Chen , C. H. Identification and prediction using recurrent compensatory neuro-fuzzy systems . Fuzzy Sets Syst. 2005 , 150 ( 2 ), 307 – 330 .
   Web of Science ®Google Scholar
• Barbounis , T. G. ; Theocharis , J. B. A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation . Neurocomputing 2007 , 70 ( 7–9 ), 1525 – 1542 .
   Google Scholar
• Takagi , H. ; Suzuki , N. ; Koda , T. ; Kojima , Y. Neural networks designed on approximate reasoning architecture and their application . IEEE Trans. Neural Networks. 1992 , 3 ( 5 ), 752 – 759 .
   Google Scholar
• Mizutani , E. ; Jang , J. S. R. Coactive neural fuzzy modeling. In Proceedings of the International Conference on Neural Networks. November 30–December 3, Perth, WA, Australia, 1995; pp. 760–765.
   Google Scholar
• Frasconi , P. ; Gori , M. ; Soda , G. Local feedback multilayered networks . Neural Comput. 1992 , 4 , 120 – 130 .
   Web of Science ®Google Scholar
• Williams , R. J. ; Zipser , D. A learning algorithm for continually running fully recurrent neural networks . Neural Comput. 1989 , 1 , 270 – 280 .
   Web of Science ®Google Scholar
• Williams , R. J. ; Peng , J. An efficient gradient-based algorithm for online training of recurrent network trajectories . Neural Comput. 1990 , 2 , 490 – 501 .
   Web of Science ®Google Scholar
• Lin , R. C. ; Weng , W. D. ; Hsueh , C. T. Design of an SCRFNN-based nonlinear channel equalizer . IEE Proc.-Commun. 2005 , 152 ( 6 ), 771 – 779 .
   Google Scholar
• Lin , F. J. ; Lin , C. H. On-line gain-tuning IP controller using RFNN . IEEE Trans. Aerosp. Electron. Syst. 2001 , 37 ( 2 ), 655 – 670 .
   Google Scholar
• Wai , R. J. Total sliding-mode controller for PM synchronous servo motor drive using recurrent fuzzy neural network. IEEE Trans. Ind. Electron. 2001, 48(5), 926–944.
   Google Scholar
• Lin , F. J. ; Fung , R. F. ; Wai , R. J. Comparison of sliding mode and fuzzy neural network control for motor-toggle servomechanism . IEEE/ASME Trans. Mechatron. 1998 , 3 , 302 – 318 .
   Google Scholar
• Zhang , J. Multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks. In Proceedings of the International Joint Conference on Neural Networks. July 24, 2003, Portland, OR, USA, vol. 1, pp. 304–309.
   Google Scholar
• Lin , F. J. ; Wang , D. H. ; Huang , P. K. RFNN controlled sensor less induction spindle motor drive . Electr. Power Syst. Res. 2004 , 70 ( 3 ), 211 – 222 .
   Google Scholar
• Nurnberger , A. ; Radetzky , A. ; Kruse , R. Using recurrent neuro-fuzzy techniques for the identification and simulation of dynamic systems . Neurocomputing. 2001 , 36 ( 1–4 ), 123 – 147 .
   Google Scholar
• Lin , F. J. ; Huang , P. K. ; Chou , W. D. Recurrent-fuzzy-neural-network-controlled linear induction motor servo drive using genetic algorithms . IEEE Trans. Ind. Electron. 2007 , 54 ( 11 ), 1449 – 1460 .
   Google Scholar
• Lin , F. J. ; Huang , P. K. ; Wang , C. C. ; Teng , L. T. An induction generator system using fuzzy modeling and recurrent fuzzy neural network . IEEE Trans. Power Electron. 2007 , 22 ( 1 ), 260 – 271 .
   Web of Science ®Google Scholar
• Lin , F. J. ; Lin , C. H. ; Shen , P. H. Self-constructing fuzzy neural network speed controller for permanent magnet synchronous motor drives . IEEE Trans. Fuzzy Syst. 2001 , 9 ( 5 ), 751 – 759 .
   Web of Science ®Google Scholar
• Lin , C. H. ; Chiang , S. J. ; Lin , M. K. Adaptive RFNN control for synchronous reluctance motor drive. In Proceedings of the 35th Annual IEEE Power Electronics Specialists Conference. Adzen, Germany 2004, pp. 3272–3277.
   Google Scholar
• Lin , C. H. Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive . IEE Proc.: Electr. Power Appl. 2004 , 151 ( 6 ), 711 – 723 .
   Google Scholar
• Lin , F. J. ; Yang , S. L. ; Shen , P. H. Self-constructing recurrent fuzzy neural network for DSP-based permanent-magnet linear-synchronous motor servo drive . IEE Proc.: Electr. Power Appl. 2006 , 153 ( 2 ), 236 – 246 .
   Google Scholar
• Wu , Q. M. ; Stanely , K. Modular neural-visual servo using a neural-fuzzy decision network. In Proceedings of IEEE International Conference on Robotics and Automation. April 20–25, 1997, Albuquerque, NM, USA, 3238–3243.
   Google Scholar
• Ballini , R. ; Gomide , F. Equality index and learning in recurrent fuzzy neural networks. In Proceedings of IEEE International Conference on Fuzzy Systems, The 12th IEEE International Conference, May 25–28, 2003, vol. 1, pp. 155–160.
   Google Scholar
• Arao , M. ; Kawaji , S. Identification of drilling system using evolving recurrent fuzzy neural networks. Presented at the IEEE International Conference on Systems, Man and Cybernetics, Tuscon, AZ, USA, Volume 3, October 7–10, 2001.
   Google Scholar
• Lee , C. H. ; Teng , C. C. Identification of dynamic system using recurrent fuzzy neural network . IEEE Trans. Fuzzy Syst. 2000 , 8 ( 4 ), 349 – 366 .
   Web of Science ®Google Scholar
• Su , S. F. ; Yang , F. Y. P. On the dynamical modeling with neural fuzzy networks . IEEE Trans. Neural Networks. 2002 , 13 ( 6 ), 1548 – 1553 .
   PubMedGoogle Scholar
• Lin , C. M. ; Chen , C. H. ; Chin , W. L. Adaptive recurrent fuzzy neural network control for linearized multivariable systems. In Proceedings of IEEE International Conference on Fuzzy Systems. July 25–29, 2004, Budapest, Hungary, 709–714.
   Google Scholar
• Lee , C. H. Stabilization of nonlinear non minimum phase systems: Adaptive parallel approach using recurrent fuzzy neural network . IEEE Trans. Syst. Man Cybern.—Part B: Cybern. 2004 , 34 ( 2 ), 1075 – 1088 .
   Google Scholar
• Lin , C. M. ; Hsu , C. F. Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings . IEEE Trans. Fuzzy Syst. 2004 , 12 ( 5 ), 733 – 742 .
   Google Scholar
• Lee , C. Y. ; Lin , C. J. ; Wang , J. ; Liao , X. ; Yi , Z. Prediction of Time Sequence Using Recurrent Compensatory Neuro-Fuzzy Systems ; Springer-Verlag : Berlin , 2005 , pp. 611 – 617 .
   Google Scholar
• Hsu , C. F. ; Lee , T. T. ; Wen , Y. W. ; Ding , F. S. Intelligent control for DC-DC power converter with recurrent fuzzy neural network approach. Presented at the IEEE International Conference on Fuzzy Systems, July 16–21, 2006, Vancouver, BC, Canada.
   Google Scholar
• Lu , C. H. ; Tsai , C. C. Generalized predictive control using recurrent fuzzy neural networks for industrial processes . J. Process Control. 2007 , 17 ( 1 ), 83 – 92 .
   Google Scholar
• Lia , C. ; Cheng , K. H. Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling . Fuzzy Sets Syst. 2007 , 158 ( 2 ), 194 – 212 .
   Google Scholar
• Lin , F. J. ; Wai , R. J. Hybrid control using recurrent fuzzy neural network for linear-induction motor servo drive. IEEE Trans. Fuzzy Syst. 2001, 9(1), 102–115.
   Google Scholar
• Yang , J. ; Chen , R. ; Fa , N. A new recurrent fuzzy neural network sliding mode position controller based on vector control of PMLSM using SVM. Presented at CES/IEEE 5th International Power Electronics and Motion Control Conference, IPEMC ‘06, Shanghai, China. August 30–September 1, 2006.
   Google Scholar
• Zhang , Q. Z. ; Gan , W. S. ; Zhou , Y. L. Adaptive recurrent fuzzy neural networks for active noise control . J. Sound Vibr. 2006 , 296 ( 4–5 ), 935 – 948 .
   Google Scholar
• Juang , C. F. ; Chen , J. S. A recurrent neural fuzzy network controller for a temperature control system. Presented at the 12th IEEE International Conference on Fuzzy Systems, May 25–28, St. Louis, MO, USA, 2003 .
   Google Scholar
• Ballini , R. ; Soares , S. ; Gomide , F. A recurrent neurofuzzy network structure and learning procedure. In Proceedings of the 10th IEEE International Conference on Fuzzy Systems, July 11–14, 2001; Vol. 3, pp. 1408–1411.
   Google Scholar
• Nurnberger , A. A hierarchical recurrent neuro-fuzzy model for system identification . Int. J. Approximate Reasoning. 2003 , 32 ( 2–3 ), 3153 – 3170 .
   Google Scholar
• Juang , C. F. ; Huang , S. T. ; Liou , Y. C. Temperature control of a rubber shoot shaping machine by recurrent neural fuzzy controller. Presented at the International Joint Conference on Neural Networks, July 16–21, 2006, Vancouver, BC, Canada.
   Google Scholar
• Theocharis , J. B. A high-order recurrent neuro-fuzzy system with internal dynamics: Application to the adaptive noise cancellation . Fuzzy Sets Syst. 2005 , 157 ( 4 ), 471 – 500 .
   Google Scholar
• Zhao , C. ; Chi , Q. ; Wang , L. ; Wen , D. B. ; Liu , A. Model Predictive Control of a Grain Dryer with Four Stages Based on Recurrent Fuzzy Neural Network ; Springer-Verlag : Berlin , pp. 29 – 37 .
   Google Scholar
• Juang , C. F. ; Huang , S. T. ; Duh , F. B. Mold temperature control of a rubber injection-molding machine by TSK-type recurrent neural fuzzy network . Neurocomputing 2006 , 70 ( 1 ), 559 – 567 .
   Google Scholar
• Gorrini , V. ; Bersini , H. Recurrent fuzzy systems. In Proceedings of the IEEE International Conference on Fuzzy Systems, June 26–29, 1994, Orlando, FL, pp. 193–198 .
   Google Scholar
• Theocharis , J. B. ; Vachtsevanos , G. Recursive learning algorithms for training fuzzy recurrent models . Int. J. Intell. Syst. 1996 , 11 ( 12 ), 1059 – 1098 .
   Google Scholar
• Juang , C. F. ; Lin , C. T. Noisy signal processing by recurrently adaptive fuzzy filters . IEEE Trans. Fuzzy Syst. 2001 , 9 ( 1 ), 139 – 152 .
   Google Scholar
• Lin , F. J. ; Wai , R. J. ; Shyu , K. K. ; Liu , T. M. Recurrent fuzzy neural network control for piezoelectric ceramic linear ultrasonic motor drive . IEEE Trans. Ultrason. Ferroelectr. Freq. Contro.l 2001 , 48 ( 4 ), 900 – 913 .
   PubMed Web of Science ®Google Scholar
• Surmann , H. ; Maniadakis , M. Learning feed forward and recurrent fuzzy systems: A genetic approach . J. Syst. Archit. 2007 , 47 ( 7 ), 649 – 662 .
   Google Scholar
• Gao , Y. ; Er , M. J. NARMAX time series model prediction: Feedforward and recurrent fuzzy neural network approaches . Fuzzy Sets Syst. 2005 , 150 ( 2 ), 331 – 350 .
   Google Scholar
• Bhat , N. V. ; McAvoy , T. J. Determining model structure for neural models by network stripping . Computers Chem. Eng . 1992 , 16 , 271 – 281 .
   Google Scholar
• Narendra , K. S. ; Parthasarathy , K. Identification and control of dynamical systems using neural networks . IEEE Trans. Neural Networks. 1990 , 1 , 4 – 27 .
   PubMedGoogle Scholar
• Takagi , T. ; Sugeno , M. Fuzzy identification of systems and its applications to modeling and control . IEEE Trans. Syst. Man Cybern. 1985 , 15 ( 1 ), 116 – 132 .
   Web of Science ®Google Scholar
• Sugeno , M. ; Kang , G. T. Structure identification of fuzzy model . Fuzzy Sets Syst. 1988 , 28 ( 1 ), 15 – 33 .
   Web of Science ®Google Scholar
• Lin , C. J. ; Chin , C. C. A wavelet-based neuro-fuzzy system and its applications. In Proceedings of the IEEE International Joint Conference on Neural Networks, July 20–24, 2003, Portland, OR, USA, 1921–1926.
   Google Scholar
• Wang , F. S. ; Lee , C. S. G. Self-adaptive neuro-fuzzy inference systems for classifications . IEEE Trans. Fuzzy Syst. 2002 , 10 ( 6 ), 790 – 801 .
   Web of Science ®Google Scholar
• Zhang , J. ; Morris , A. J. Long range predictive control of nonlinear processes based on recurrent neuro-fuzzy network models . Neural Comput. Appl. 2000 , 9 ( 1 ), 50 – 59 .
   Google Scholar
• Olvera , M. A. G. ; Tang , Y. A new recurrent neuro fuzzy network for identification of dynamic systems . Fuzzy Sets Syst. 2007 , 158 ( 10 ), 1023 – 1035 .
   Google Scholar
• Lin , F. J. ; Shyu , K. K. ; Wai , R. J. Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggle servomechanism . IEEE/ASME Trans. Mechatron. 2001 , 6 ( 4 ), 453 – 466 .
   Google Scholar
• Lin , F. J. ; Lin , C. H. ; Huang , P. K. Recurrent fuzzy neural network controller design using sliding-mode control for linear synchronous motor drive. IET Control Theory Appl. 2004, 151(4), 407–416.
   Google Scholar
• Lin , F. J. ; Lin , C. H. ; Shen , P. H. Variable-structure control for a linear synchronous motor using a recurrent fuzzy neural network . IET Control Theory Appl. 2004 , 151 ( 4 ), 395 – 406 .
   Google Scholar
• Mihai , D. Discrete fuzzy control loops based on a motor neuro-fuzzy model: Pushing too far a continuous logic? IEEE International Conference on Fuzzy Systems, July 25–29, 2004, Budapest, Hungary.
   Google Scholar
• Chiang , S. J. ; Lin , C. H. Adaptive back stepping RFNN control for synchronous reluctance motor drive. Presented at the 37th IEEE Power Electronics Specialists Conference, PESC '06, June 18–22, 2006, IEEE:10.1109/PESC.2006.1711846, 1–6.
   Google Scholar
• Lin , C. H. ; Chiang , S. J. Torque-ripple reduction in switched reluctance motor drive using SHRFNN control. Presented at the 37th IEEE Power Electronics Specialists Conference, PESC '06, June 18–22, 2006, IEEE:10.1109/PESC.2006.1712262, pp. 1–6.
   Google Scholar
• Marcos , A. ; Olvera , G. ; Tang , Y. ; Wang , J. A New Recurrent Neuro Fuzzy Network for Identification of Dynamic Systems ; Springer-Verlag : Berlin , pp. 796 – 801 .
   Google Scholar
• Lee , J. W. ; Suh , J. H. ; Lee , Y. J. ; Lee , K. S. Position accuracy improvement of linear motor container transfer system using DR-FNNs . In Proceedings of the 2004 IEEE International Conference on Control Applications ; September 2–4, 2004, Taipei, Taiwan .
   Google Scholar
• Maraziotis , I. ; Dragomir , A. ; Bezerianos , A. Recurrent Neuro-Fuzzy Network Models for Reverse Engineering Gene Regulatory Interactions ; Springer-Verlag : Berlin , 2005 , pp. 24 – 34 .
   Google Scholar
• Maraziotis , I. ; Dragomir , A. ; Bezerianos , A. Gene networks inference from expression data using a recurrent neuro-fuzzy approach. In IEEE Proceedings of Engineering in Medicine and Biology, 27th Annual Conference, Jan. 17–18, 2006, pp. 4834–4837.
   Google Scholar
• Mastorocostas , P. A. ; Theocharis , J. B. A recurrent fuzzy-neural filter for real-time separation of lung sounds. In Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 5, July 31–August 4, 2005, pp. 3023–3028.
   Google Scholar
• Wu , S. N. ; Wang , J. S. An adaptive recurrent neuro-fuzzy filter for noisy speech enhancement. In Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 4, July 25–29, 2004, Budapest, Hungary.
   Google Scholar
• Lin , C. T. , Wu , R. C. ; Chang , J. Y. ; Liang , S. F. A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system . IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2004 , 34 ( 1 ), 309 – 324 .
   PubMed Web of Science ®Google Scholar
• Juang , C. F. ; Lai , C. L. ; Tu , C. C. Dynamic programming prediction errors of recurrent neural fuzzy networks for speech recognition . Expert Syst. Appl. 2009 , 36 ( 3 ), 6368 – 6374 .
   Google Scholar
• Stavrakoudis , D. G. ; Theocharis , J. B. Pipelined recurrent fuzzy neural networks for nonlinear adaptive speech prediction . IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2007 , 37 ( 5 ), 1305 – 1320 .
   Google Scholar
• Evsukoffa , A. ; Gentil , S. Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors . Adv. Eng. Inf. 2005 , 19 ( 1 ), 55 – 66 .
   Google Scholar
• Kumar , S. ; Kumar , S. ; Prakash ; Shankar , R. ; Tiwari , M. K. ; Kumar , S. B. Prediction of flow stress for carbon steels using recurrent self-organizing neuro fuzzy networks . Expert Syst. Appl. 2007 , 32 ( 3 ), 777 – 788 .
   Google Scholar
• Chiul , C. S. ; Chiang , T. S. Recurrent TS fuzzy neural network and its application on controlling nonlinear time-delay systems . In Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence (FOCI) ; April, 1–5 , 2007 ; pp. 460 – 465 .
   Google Scholar
• Aliev , R. A. ; Aliev , R. R. ; Guirimov , B. G. ; Uyar , K. ; Liu , D. Recurrent Fuzzy Neural Network Based System for Battery Charging ; Springer-Verlag : Berlin , Part II , 307 – 316 .
   Google Scholar
• Lee , C. H. ; Chiu , M. H. Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm . Expert Syst. Appl. 2009 , 36 ( 5 ), 8993 – 8999 .
   Google Scholar
• Huang , Y. R. ; Kang , Y. ; Chu , M. H. ; Chien , S. Y. ; Chang , Y. P. Modified recurrent neuro-fuzzy network for modeling ball-screw servomechanism by using Chebyshev polynomial . Expert Syst. Appl. 2009 , 36 ( 3 ), 5317 – 5326 .
   Google Scholar