Local Linear Model Tree (LOLIMOT) Reconfigurable Parallel Hardware
Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.
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applications to modeling and control," IEEE Tran. Systems, Man and
Cybernetics, vol. 15, pp. 116-132, 1985.
[2] J. R. Jang, "ANFIS: Adaptive network based fuzzy inference system,"
IEEE Tran. Systems, Man and Cybernetics, vol. 23, no. 3, 1993, pp. 665-
685.
[3] O. Nelles, Nonlinear system identification. Berlin: Springer Verlag,
2001.
[4] C. Lucas, R. M. Milasi and B. N.Araabi, "Intelligent modeling and
control of washing machine using LLNF modeling and modified
BELBIC," Asian Journal of Control, vol.8, no.4, December 2005.
[5] O. Nelles, "Local linear model tree for on-line identification of time
variant nonlinear dynamic systems," International Conference on
Artificial Neural Networks (ICANN), pp. 115-120, Bochum-Germany,
1996.
[6] L. M. Reyneri, "Implementation issues of neuro-fuzzy hardware: Going
toward hw/sw codesign," IEEE Trans Neural Networks, vol. 14, no. 1,
pp. 176-194, Jan. 2003.
[7] S. M. Fakhraie and K. C. Smith, VLSI-Compatible Implementations for
Artificial Neural Networks. Norwell, Massachusetts: Kluwer Academic
Publishers, 1997.
[8] L. M. Reyneri, Neuro-fuzzy hardware: design, development and
performance, in Proc. Of FEPPCON III, Skukuza (South Africa), 12-15
July 1998.
[9] O. Nelles, "Nonlinear system identification with local linear neuro-fuzzy
models," PhD Thesis, TU Darmstadt, Shaker Verlag, Aachen, Germany,
1999.
[10] C. H. Lee, W. Y. Lai, C. C. Chen, "Lossless image coding via adaptive
Takagi-Sugeno fuzzy neural network predictor", Proc. of the. 2004,
IEEE. International Conference on Networking, Sensing. &. Conrrol.
Taipei,. Taiwan, March. 21-23, 2004.
[11] I. Nedeljkovic, "Image classification based on fuzzy logic," in Proc.
Geo-Imagery Bridging Continents XXth ISPRS Congress, 12-23, Turkey,
Istanbul, july, 2004.
[12] W. Xiaofang, S. G. Ziavras," Performance optimization of an FPGAbased
configurable multiprocessor for matrix operations," Proc. 2003
IEEE International Conference on Field-Programmable Technology
(FPT), pp 303 - 3-6, dec. 2003.
[13] B. P. Flannery, Numerical Recipes in C/C++, William H. Press, 2005.
[14] T. Abbasian, F.R. Salmasi, M.J. Yazdanpanah, "Stability analysis of
sensorless IM based on adaptive feedback linearization control with
unknown stator and rotor resistances," Industry Applications
Conference, 2005. Fourtieth IAS Annual Meeting, vol. 2, pp 985 - 992,
Oct. 2005.
[1] T. Takagi, M. Sugeno, "Fuzzy identification of systems and its
applications to modeling and control," IEEE Tran. Systems, Man and
Cybernetics, vol. 15, pp. 116-132, 1985.
[2] J. R. Jang, "ANFIS: Adaptive network based fuzzy inference system,"
IEEE Tran. Systems, Man and Cybernetics, vol. 23, no. 3, 1993, pp. 665-
685.
[3] O. Nelles, Nonlinear system identification. Berlin: Springer Verlag,
2001.
[4] C. Lucas, R. M. Milasi and B. N.Araabi, "Intelligent modeling and
control of washing machine using LLNF modeling and modified
BELBIC," Asian Journal of Control, vol.8, no.4, December 2005.
[5] O. Nelles, "Local linear model tree for on-line identification of time
variant nonlinear dynamic systems," International Conference on
Artificial Neural Networks (ICANN), pp. 115-120, Bochum-Germany,
1996.
[6] L. M. Reyneri, "Implementation issues of neuro-fuzzy hardware: Going
toward hw/sw codesign," IEEE Trans Neural Networks, vol. 14, no. 1,
pp. 176-194, Jan. 2003.
[7] S. M. Fakhraie and K. C. Smith, VLSI-Compatible Implementations for
Artificial Neural Networks. Norwell, Massachusetts: Kluwer Academic
Publishers, 1997.
[8] L. M. Reyneri, Neuro-fuzzy hardware: design, development and
performance, in Proc. Of FEPPCON III, Skukuza (South Africa), 12-15
July 1998.
[9] O. Nelles, "Nonlinear system identification with local linear neuro-fuzzy
models," PhD Thesis, TU Darmstadt, Shaker Verlag, Aachen, Germany,
1999.
[10] C. H. Lee, W. Y. Lai, C. C. Chen, "Lossless image coding via adaptive
Takagi-Sugeno fuzzy neural network predictor", Proc. of the. 2004,
IEEE. International Conference on Networking, Sensing. &. Conrrol.
Taipei,. Taiwan, March. 21-23, 2004.
[11] I. Nedeljkovic, "Image classification based on fuzzy logic," in Proc.
Geo-Imagery Bridging Continents XXth ISPRS Congress, 12-23, Turkey,
Istanbul, july, 2004.
[12] W. Xiaofang, S. G. Ziavras," Performance optimization of an FPGAbased
configurable multiprocessor for matrix operations," Proc. 2003
IEEE International Conference on Field-Programmable Technology
(FPT), pp 303 - 3-6, dec. 2003.
[13] B. P. Flannery, Numerical Recipes in C/C++, William H. Press, 2005.
[14] T. Abbasian, F.R. Salmasi, M.J. Yazdanpanah, "Stability analysis of
sensorless IM based on adaptive feedback linearization control with
unknown stator and rotor resistances," Industry Applications
Conference, 2005. Fourtieth IAS Annual Meeting, vol. 2, pp 985 - 992,
Oct. 2005.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51271", author = "A. Pedram and M. R. Jamali and T. Pedram and S. M. Fakhraie and C. Lucas", title = "Local Linear Model Tree (LOLIMOT) Reconfigurable Parallel Hardware", abstract = "Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.
", keywords = "LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.", volume = "2", number = "1", pages = "25-6", }
