### Comparative Study of Non Linear System Modeling Using Artificial Intelligent Techniques

#### Abstract

#### Keywords

#### Full Text:

PDF#### References

Kong S.G. and Kosko B. (1990), ‘Comparison of Fuzzy and Neural Truck Backer-Upper Control System’, In:IEEE International Conference on Information Acquisition, vol.3, pp.349-358.

Claudio Altafini, Alberto Speranzon, and Bo Wahlberg (2001), ‘A Feedback Control Scheme for Reversing a Truck and Trailer Vehicle’, In:IEEE transactions on robotics and automation, vol. 17, No. 6, pp.915-922.

Dong-hai Zhai, Li Li and Fan Jin (2003), ‘Fuzzy Neural Network for Nonlinear-Systems Model Identification’, In:IEEE International Symposium on Computational Intelligence in Robotics and Automation, vol.3, pp.1282-1287.

Nguyen D. and Widrow B. (1990), ‘Neural Networks for Self-Learning Control Systems’, In:IEEE, Control system magazine, vol.10, No.3, pp.18-23.

Fodor D., Six J.P., and Diana D. (1995), ‘Neural network applied for induction motor speed sensor-less estimation’, Proceedings ISTE, pp.181-186.

Gopala Krishna Rao C.V,., Bapi Raju V and Ravindranath G. (2009), ‘Fuzzy Load Modeling and Load Flow Study Using Radial Basis Function (RBF)’, Journal of Theoretical and Applied Information Technology.

Henneberger G., Bransbashand B.J. and Klepsch Th. (1991), ’Field oriented control Of synchronous and asynchronous drives without mechanical sensors using Kalman Filter’, in Proceedings of EPE, Florence, Italy, PP.664-671.

Jang J.S.R., Sun C.T., Mizutani E. (2008), ‘Neuro-fuzzy And Soft Computing, A Computational Approach to Learning and Machine Intelligence’, Edition I, Prentice-Hall, India.

Jui-Jung Liu, I-Chung Kung and Hui-Cheng Cho (2001), ‘Speed estimation of induction motor using a non- linear identification techniques’, Proceedings National science, counseling Roc (A), vol.25, No.2, pp.107- 111.

Suresh S., Omkar S.N., Mani V. and Guruprakash T.N.(2003), ’Lift Coefficient prediction at high angle of attack using recurrent neural network’, Aerospace & technology 7, pp.595-602.

Narendra K.S. and Mukhopadhya S. (1997), ‘Adaptive Control Using Neural Networks and Approximate Models’, In: IEEE Transactions on Neural Networks, vol. 8, No. 3, pp.475-485.

Doh-Hyun Kim and Jun-Ho Oh (1999), ‘Experiments of Backward Tracking Control for Trailer System’, In:IEEE International Conference on Robotics & Automation Detroit, Michigan, vol.1, pp.19-22.

Ohtani T., Takada N. and Tanaka K. (1989),’Vector control of induction motor without shaft encoder”, in conf. Rec. IEEE - IAS. Annual Meeting, pp.500-507.

Robert E. Jenkins and Ben P. Yuhas(1993), ‘A Simplified Neural Network Solution Through Problem Decomposition: The Case of the Truck Backer-Upper’, In:IEEE transactions on neural networks, vol. 4, No. 4, pp.718-720.

Satish Kumar A. (2005), ‘Neural Networks-A Class Room Approach’, Edition I, Tata Mcgraw Hill.

Saidi Nabiha and Messaoud Hassani (2008), ‘A Comparative Study of Non Linear System Models’, In:IEEE International Conference on Information Acquisition, vol.12, pp.417-420.

Schauder C. (1989), ‘Adaptive Speed identification for vector control of induction motor without rotational transducers’, in conf. Rec. IEEE - IAS. Annual Meeting, pp.493-499.

Seong –HwanKim, Tae-Sik park, Ji-yoon .yoo & Gwi-tae Park (2001),’ Speed sensor less vector control of an induction motor using neural network speed estimation’, IEEE. Transactions on Industrial Electronics.48.No.3.

Simon Haykin (1999), ‘Neural Networks-A Comprehensive Foundation’, Edition II, Presentice-Hall, India.

Michael T., Wishart & Ronald, Harley G. (1995), ‘Identification & Control of induction motor using artificial neural network’, IEEE. Transactions on industrial Applications, vol.31. No.3.

Tamia H. and Hori Y. (1993), ‘Speed sensor less field orientation control of the induction machine”, IEEE. Transactions on Industrial Applications, vol.29, pp.175-180.

Yi Liao (1999), ‘A Neural Network Approach to Solve the Truck Backer-Upper and Iris Flower Problems’, Journal of Intelligent and Fuzzy System.

L. A. Zadeh, "Fuzzy sets," Inform. Control, vol. 8, no. 3, pp. 338-353, 1965.

D. Rutkowska, M. Pilinski, and L. Rutkowski, Neural Networks, Genetic Algorithms and Fuzzy Systems. Warsaw, Poland: PWN Scientific, 1997. [16] R. R. Yager and D. P. Filev, "Relational partitioning of fuzzy rules,"Fuzzy Sets Syst, vol. 80, pp. 57-69, 1996.

E. Kim, M. Park, S. Kim, and M. Park, "A transformed input-domain ap-proach to fuzzy modeling," IEEE Trans. Fuzzy Syst., vol. 6, pp. 596-604, Aug. 1998.

R. S. Crowder, "Predicting the Mackey-Glass time series with cascade-correlation learning," in Proc. 1990 Connectionist Models Summer School, D. Touretzky, J. Elman, G. Hinton, and T. Sejnowski, Eds., San Mateo, CA, 1990, pp. 117-123.

L. Wang and R. Langari, "Building sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques," IEEE Trans. Fuzzy Syst., vol. 3, pp. 454-458, Aug. 1995.

H. Inoue, K. Kamei, and K. Inoue, "Auto-generation of fuzzy production rules using hyper elliptic cone membership function by genetic algorithm," in Proc. 4th Int. Conf. Soft Computing, IIZUKA '96, Fukuoka, Japan, Sept.-Oct. 30-5, 1996, pp. 82-85.

K. Hornik, M. Stinchcombe and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, vol. 2, pp. 359-366, 1989.

R. J. Machado and A. F. Rocha, "A hybrid architecture for fuzzy con-nectionist expert systems," in Intelligent Hybrid Systems, A. Kandel and G. Langholz, Eds. Boca Raton, FL: CRC, 1992, pp. 136-152.

F. C. H. Rhee and R. Krishnapuram, "Fuzzy rule generation methods for high-level computer vision," Fuzzy Sets Syst, vol. 60, pp. 245-258, 1993.

L. Y Cai and H. K. Kwan, "Fuzzy classifications using fuzzy inference networks,"IEEE Trans. Syst, Man, Cybern., vol. 28, pp. 334-347,1998.

K. B. Cho and B. H. Wang, "Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction," Fuzzy Sets Syst, vol. 83, pp. 325-339, 1996.

DOI: http://dx.doi.org/10.36039/AA082010006

### Refbacks

- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution 3.0 License.