A Neurofuzzy Learning and its Application to Control System
A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.
[1] R.R.Yager and D.P.Filev, "Generation of fuzzy rules by mountain
clustering", Journal of Intelligent and Fuzzy System, vol.2, pp. 209-219,
1994.
[2] S.L.Chiu, "Fuzzy model identification based on cluster estimation",
Journal of Intelligent and Fuzzy System, vol.2, pp. 267-278, 1994
[3] S. Chiu, "Extracting fuzzy rules from data for function approximation
and pattern classification," in Chapter 9 in Fuzzy Set Methods in
Information Engineering: A Guided Tour of Applications, ed. D. Dubois,
H. Prade, and R. Yager, John Wiley, 1997.
[4] Peter Grabusts, "Clustering Methods in Neuro - Fuzzy Modelling",
Available:
http://www.dssg.cs.rtu.lv/download/publications/2002/Garbusts-RA-
2002.pdf.
[5] Petr Pivonka, "Comparative analysis of Fuzzy PI/PD/PID controller
based on classical PID controller approach". Available:
http://www.feec.vutbr.cz/~pivonka/.
[6] Chia-Feng Juang and Chin-Teng Lin, An On-Line Self-Constructing
Neural Fuzzy Inference Network and Its Applications, IEEE Trans.
Fuzzy System ,vol. 6, no.2, pp.13-31, 1999.
[7] Seema Chopra, R. Mitra and Vijay Kumar, "Identification of Rules
using Subtractive Clustering with Application to Fuzzy Controllers,"
Proc. of the Third International Conference on Machine Learning and
Cybernetics, 2004, pp.4125-4131.
[8] D. Driankov, H. Hellendorn, and M. Reinfrank, An Introduction to
Fuzzy Control. New York: Springer-Verlag, 1993.
[9] Seema Chopra, R. Mitra and Vijay Kumar, "Identification of Self-
Tuning Fuzzy PI type controllers with reduced rule set", Proc. of the
IEEE International Conference on Networking, Sensing and Control,
Mar., 2005, pp. 537-542.
[10] J. R. Yang, "ANFIS: Adaptive-Network-Based Fuzzy Inference
System", IEEE Trans. on systems, man, and cybernetic, vol. 23, no. 3,
pp. 665-685, May 1993.
[11] K Tanaka, M Sano and H. Watanabe, "Modelling and Control of Carbon
Monoxide Concentration using a Neurofuzzy technique," IEEE Trans.
Fuzzy System., vol 3, no 3, pp. 271-279, Aug. 1999.
[12] T. Takagi, M. Sugeno, "Fuzzy identification of systems and its
application to modeling and control", IEEE Trans. on Systems, Man, and
Cybernetics, vol. 15, pp. 116-132, 1985.
[13] Kevin M.Passino and Stephen Yurkovich, "Fuzzy Control", Addison
Wesley Longman, Inc., California, 1998.
[14] A. Nurnberger, D. Nauck and R Kruse, "Neuro-fuzzy control based on
the Nefcon-model: recent developments", Soft Computing, vol. 2, 1999,
pp. 168-182.
[15] Mohammad Fazle Azeem, Madasu Hanmandlu, Nesar Ahmed,
"Structure identification of Generalized Adaptive Neuro-Fuzzy
Inference Systems," IEEE Trans. Fuzzy System., vol 11, no. 5, Oct.
2003, pp. 666-681.
[16] Shie-Jue Lee and Chen-Sen Ouyang, "A Neuro-fuzzy system modeling
with Self-Constructing Rule Generation and Hybrid SVD-Based
Learning," IEEE Ttrans. Fuzzy System., vol. 11, no. 3, pp. 341-353, Jun.
2003.
[17] Chin-Teng Lin and C. S. George Lee, "Reinforcement Structure/
Parameter Learning for Neural Network based Fuzzy Logic Control
Systems," IEEE Ttrans. Fuzzy System., vol 2, no 1, pp.46-73, Feb. 1994.
[18] G. Castellano and A.M. Fanelli, "Fuzzy inference and rule extraction
and using neural network". URL
[19] Chin-Teng Lin and Ya-Ching Lu, "A Neural Fuzzy System with fuzzy
supervised learning", IEEE Trans. on systems, man, and cybernetic-Part
B, vol. 26, no. 5, pp. 744-763, Oct. 1996.
[20] R.K.Mudi and N.R.Pal, A robust self-tuning scheme for PI and PD type
fuzzy controllers, IEEE Trans. Fuzzy System, vol. 7, no.1, pp. 2-16,
1999.
[21] Kuhu Pal, Rajani K. Mudi, and Nikhil R. Pal, "A New Scheme for
Fuzzy Rule-Based System Identification and Its Application to Self-
Tuning Fuzzy Controllers", IEEE Trans .on systems, man, and
cyberneticsÔÇöpart b: cybernetics, vol. 32, no. 4, Aug. 2002.
[22] Seema Chopra, R. Mitra and Vijay Kumar, "Fuzzy controller -
Choosing an appropriate and smallest rule set", International Journal on
Computational Cognition, vol 3, no. 4, pp. 73-79, Dec 2005.
[23] Seema Chopra, R. Mitra and Vijay Kumar, "Analysis and Design of
Fuzzy Controller using Neurofuzzy Techniques" Proc. of the
International Conference on Computer Applications in Electrical
Engineering Recent Advances, CERA 2005, Sep, 2005, pp 39-43.
[24] Seema Chopra, R Mitra, Vijay Kumar, "Analysis and Design of Fuzzy
models using Fuzzy Curves", Proceedings of the Second Conference on
Intelligent systems & networks, Jagdhari, Feb. 2005, pp. 65-71.
[25] Seema Chopra, R. Mitra and Vijay Kumar, "Analysis of Fuzzy PI and
PD type controllers using subtractive clustering", International Journal
on Computational Cognition, to appear in vol 4, no.1, 2006.
[26] Process Explorer - Sysinternals software. Available:
www.sysinternals.com.
[27] Kasabov, N., "Learning fuzzy rules and approximate reasoning in
fuzzy neural networks and hybrid systems", Fuzzy Sets and Systems
vol. 82 no. 2 pp. 2- 20, 1996.
[28] Ferry Marga Caswara and Heinz Unbehauen, "A Neurofuzzy Approach
to the Control of a Flexible-Link Manipulator,"IEEE Trans. On Robotics
And Automation, vol. 18, no. 6, pp. 932-944, Dec. 2002.
[29] Sudath R. Munasinghe,Min-Soeng Kim and Ju-Jang Lee, "Adaptive
Neurofuzzy Controller to Regulate UTSG Water Level in Nuclear Power
Plants," IEEE Trans. On Nuclear Science, vol. 52, vo. 1, pp. 421-429,
Feb. 2005.
[30] Joseph P. Geisler, C. S. George Lee, and Gary S. May, "Neurofuzzy
Modeling of Chemical Vapor Deposition Processes," IEEE Trans. on
Semiconductor Manufacturing, vol. 13, no. 1, pp. 46-60, Feb. 2000.
[31] William W. Melek and Andrew A. Goldenberg, "Neurofuzzy Control of
Modular and Reconfigurable Robots," IEEE/ASME Trans. on
Mechatronics, vol. 8, no. 3, pp 381-389, Sep. 2003.
[32] Chen-Sen Ouyang, Wan-Jui Lee, and Shie-Jue Lee, "A TSK-Type
Neurofuzzy Network Approach to System Modeling Problems," IEEE
Trans. on Systems, Man, and CyberneticsÔÇöPart B: Cybernetics, vol. 35,
no. 4, pp. 751-767, Aug. 2005.
[1] R.R.Yager and D.P.Filev, "Generation of fuzzy rules by mountain
clustering", Journal of Intelligent and Fuzzy System, vol.2, pp. 209-219,
1994.
[2] S.L.Chiu, "Fuzzy model identification based on cluster estimation",
Journal of Intelligent and Fuzzy System, vol.2, pp. 267-278, 1994
[3] S. Chiu, "Extracting fuzzy rules from data for function approximation
and pattern classification," in Chapter 9 in Fuzzy Set Methods in
Information Engineering: A Guided Tour of Applications, ed. D. Dubois,
H. Prade, and R. Yager, John Wiley, 1997.
[4] Peter Grabusts, "Clustering Methods in Neuro - Fuzzy Modelling",
Available:
http://www.dssg.cs.rtu.lv/download/publications/2002/Garbusts-RA-
2002.pdf.
[5] Petr Pivonka, "Comparative analysis of Fuzzy PI/PD/PID controller
based on classical PID controller approach". Available:
http://www.feec.vutbr.cz/~pivonka/.
[6] Chia-Feng Juang and Chin-Teng Lin, An On-Line Self-Constructing
Neural Fuzzy Inference Network and Its Applications, IEEE Trans.
Fuzzy System ,vol. 6, no.2, pp.13-31, 1999.
[7] Seema Chopra, R. Mitra and Vijay Kumar, "Identification of Rules
using Subtractive Clustering with Application to Fuzzy Controllers,"
Proc. of the Third International Conference on Machine Learning and
Cybernetics, 2004, pp.4125-4131.
[8] D. Driankov, H. Hellendorn, and M. Reinfrank, An Introduction to
Fuzzy Control. New York: Springer-Verlag, 1993.
[9] Seema Chopra, R. Mitra and Vijay Kumar, "Identification of Self-
Tuning Fuzzy PI type controllers with reduced rule set", Proc. of the
IEEE International Conference on Networking, Sensing and Control,
Mar., 2005, pp. 537-542.
[10] J. R. Yang, "ANFIS: Adaptive-Network-Based Fuzzy Inference
System", IEEE Trans. on systems, man, and cybernetic, vol. 23, no. 3,
pp. 665-685, May 1993.
[11] K Tanaka, M Sano and H. Watanabe, "Modelling and Control of Carbon
Monoxide Concentration using a Neurofuzzy technique," IEEE Trans.
Fuzzy System., vol 3, no 3, pp. 271-279, Aug. 1999.
[12] T. Takagi, M. Sugeno, "Fuzzy identification of systems and its
application to modeling and control", IEEE Trans. on Systems, Man, and
Cybernetics, vol. 15, pp. 116-132, 1985.
[13] Kevin M.Passino and Stephen Yurkovich, "Fuzzy Control", Addison
Wesley Longman, Inc., California, 1998.
[14] A. Nurnberger, D. Nauck and R Kruse, "Neuro-fuzzy control based on
the Nefcon-model: recent developments", Soft Computing, vol. 2, 1999,
pp. 168-182.
[15] Mohammad Fazle Azeem, Madasu Hanmandlu, Nesar Ahmed,
"Structure identification of Generalized Adaptive Neuro-Fuzzy
Inference Systems," IEEE Trans. Fuzzy System., vol 11, no. 5, Oct.
2003, pp. 666-681.
[16] Shie-Jue Lee and Chen-Sen Ouyang, "A Neuro-fuzzy system modeling
with Self-Constructing Rule Generation and Hybrid SVD-Based
Learning," IEEE Ttrans. Fuzzy System., vol. 11, no. 3, pp. 341-353, Jun.
2003.
[17] Chin-Teng Lin and C. S. George Lee, "Reinforcement Structure/
Parameter Learning for Neural Network based Fuzzy Logic Control
Systems," IEEE Ttrans. Fuzzy System., vol 2, no 1, pp.46-73, Feb. 1994.
[18] G. Castellano and A.M. Fanelli, "Fuzzy inference and rule extraction
and using neural network". URL
[19] Chin-Teng Lin and Ya-Ching Lu, "A Neural Fuzzy System with fuzzy
supervised learning", IEEE Trans. on systems, man, and cybernetic-Part
B, vol. 26, no. 5, pp. 744-763, Oct. 1996.
[20] R.K.Mudi and N.R.Pal, A robust self-tuning scheme for PI and PD type
fuzzy controllers, IEEE Trans. Fuzzy System, vol. 7, no.1, pp. 2-16,
1999.
[21] Kuhu Pal, Rajani K. Mudi, and Nikhil R. Pal, "A New Scheme for
Fuzzy Rule-Based System Identification and Its Application to Self-
Tuning Fuzzy Controllers", IEEE Trans .on systems, man, and
cyberneticsÔÇöpart b: cybernetics, vol. 32, no. 4, Aug. 2002.
[22] Seema Chopra, R. Mitra and Vijay Kumar, "Fuzzy controller -
Choosing an appropriate and smallest rule set", International Journal on
Computational Cognition, vol 3, no. 4, pp. 73-79, Dec 2005.
[23] Seema Chopra, R. Mitra and Vijay Kumar, "Analysis and Design of
Fuzzy Controller using Neurofuzzy Techniques" Proc. of the
International Conference on Computer Applications in Electrical
Engineering Recent Advances, CERA 2005, Sep, 2005, pp 39-43.
[24] Seema Chopra, R Mitra, Vijay Kumar, "Analysis and Design of Fuzzy
models using Fuzzy Curves", Proceedings of the Second Conference on
Intelligent systems & networks, Jagdhari, Feb. 2005, pp. 65-71.
[25] Seema Chopra, R. Mitra and Vijay Kumar, "Analysis of Fuzzy PI and
PD type controllers using subtractive clustering", International Journal
on Computational Cognition, to appear in vol 4, no.1, 2006.
[26] Process Explorer - Sysinternals software. Available:
www.sysinternals.com.
[27] Kasabov, N., "Learning fuzzy rules and approximate reasoning in
fuzzy neural networks and hybrid systems", Fuzzy Sets and Systems
vol. 82 no. 2 pp. 2- 20, 1996.
[28] Ferry Marga Caswara and Heinz Unbehauen, "A Neurofuzzy Approach
to the Control of a Flexible-Link Manipulator,"IEEE Trans. On Robotics
And Automation, vol. 18, no. 6, pp. 932-944, Dec. 2002.
[29] Sudath R. Munasinghe,Min-Soeng Kim and Ju-Jang Lee, "Adaptive
Neurofuzzy Controller to Regulate UTSG Water Level in Nuclear Power
Plants," IEEE Trans. On Nuclear Science, vol. 52, vo. 1, pp. 421-429,
Feb. 2005.
[30] Joseph P. Geisler, C. S. George Lee, and Gary S. May, "Neurofuzzy
Modeling of Chemical Vapor Deposition Processes," IEEE Trans. on
Semiconductor Manufacturing, vol. 13, no. 1, pp. 46-60, Feb. 2000.
[31] William W. Melek and Andrew A. Goldenberg, "Neurofuzzy Control of
Modular and Reconfigurable Robots," IEEE/ASME Trans. on
Mechatronics, vol. 8, no. 3, pp 381-389, Sep. 2003.
[32] Chen-Sen Ouyang, Wan-Jui Lee, and Shie-Jue Lee, "A TSK-Type
Neurofuzzy Network Approach to System Modeling Problems," IEEE
Trans. on Systems, Man, and CyberneticsÔÇöPart B: Cybernetics, vol. 35,
no. 4, pp. 751-767, Aug. 2005.
@article{"International Journal of Information, Control and Computer Sciences:52493", author = "Seema Chopra and R. Mitra and Vijay Kumar", title = "A Neurofuzzy Learning and its Application to Control System", abstract = "A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.
", keywords = "Fuzzy control, neuro-fuzzy techniques, fuzzy subtractive clustering, extraction of rules, and optimization of membership functions.", volume = "1", number = "10", pages = "3018-7", }
