Continuous CMAC-QRLS and its systolic array

Neural Processing Letters

Volumn 22, Issue 1, 2005, Pages 1-16

  Qin, Ting ^a   Zhang, Haitao ^a   Chen, Zonghai ^a   Xiang, Wei ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

B splines; CMAC QRLS; QR decomposition; Systolic array

Indexed keywords

ALGORITHMS; COMPUTER HARDWARE; COMPUTER SIMULATION; COMPUTER SOFTWARE; CONVERGENCE OF NUMERICAL METHODS; LEAST SQUARES APPROXIMATIONS; PATTERN RECOGNITION; SIGNAL PROCESSING;

B-SPLINES; CEREBRAL MODEL ARTICULATION CONTROLLER (CMAC); CMAC-QRLS; LEAST MEAN SQUARE RULE (LMS); QR DECOMPOSITION;

SYSTOLIC ARRAYS;

EID: 23844524257     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11063-004-2694-0     Document Type: Article

References (20)

1. Albus, J. S.: A new approach to manipulator control: The cerebellar model articulation controller (CMAC), Trans. ASME, J. Dyn. Syst., Meas. Control, 97 (1975), 220-227.
2. Albus, J. S.: Data storage in the cerebellar model articulation controller (CMAC), Trans. ASME, J. Dyn. Syst. Means. Control, 97 (1975), 228-233.
3. Chiang, C. -T. and Lin, C. -S.: CMAC with general basis functions, Neural Netw., 9 (1996), 1199-1211.
4. Lane, S. H., Handelman, D. A. and Gelfand, J. J.: Theory and development of higher-order CMAC neural networks, IEEE Control Syst. Mag., 12 (1992), 23-30.
5. Parks, P. C. and Militzer, J.: Convergence properties of associative memory storage for learning control system, Automat. Rem. Control, 50 (1989), 254-286.
6. Wong, Y. -F. and Siders, A.: Learning convergence in the cerebellar model articulation controller, IEEE Trans. Nerural Netw., 3 (1992), 115-121.
7. Lin, C. -S. and Chiang, C. -T.: Learning convergence of CMAC technique, IEEE Trans. Neural Netw., 8 (1997), 1281-1292.
8. He, C., Xu, L. and Zhang, Y.: Learning Convergence of CMAC Algorithm, Neural Process. Lett., 14 (2001), 61-74.
9. Jar-Shone, K., Hwang, K. Y. and Bin-Da, L.: Hardware realization of higher-order CMAC model for color calibration, Proceedings of IEEE International Conference on Neural Netwokrs, Perth, WA, Australia, 11/27/1995-12/01/1995, pp. 1656-1661.
10. Miller, W. T., Latham, P. J. and Scalera, S. M.: Bipedal Gait Adaptation for Walking with Dynamic Balance, Proceedings of the 1991 American Controls Conference, Boston, MA, June, 1991. Vol. 2, pp. 1603-1608.
11. Miller, W T., Glanz, F. H. and Kraft, L. G.: Application of a general learning algorithm to the control of robotic manipulators, Int. J. Robot. Res., Vol.6, pp. 84-98, 1987.
12. Miller, W. T.: Real-time application of neural networks for sensor-based control of robots with vision, IEEE Trans. Syst., Man Cybernetics, 19 (1989), 825-831
13. Miller, W. T., Hewes, R. P., Glanz, F. H. and Kraft, L. G.: Real-time dynamic control of an industrial manipulator using a neural-network-based learning controller, IEEE Trans. Robotics Automat 6 (1990), 1-9
14. Manglevhedakar, S.: An adaptive hierarchical model for computer vision. Thesis, Louisiana State University. 1986.
15. Kolcz, A. and Allinson, N. M.: Application of the CMAC input encoding scheme in the N-tuple approximation network, IEE Proc. Comput. Digit Tech., 141 (1994), 177-183
16. Qin, T. and Chen, Z.: A learning algorithm of CMAC based On RLS, Neural Process. Lett., 19 (2004), 49-61
17. Haykin, S.: Adaptive Filter Theory, Prentice Hall, 2002.
18. Yang, B. and Bohme, J. F.: Rotation-based RLS algorithms: Unified derivations, numerical properties, and parallel implementations, IEEE Trans. Signal Process., 40 (1992), 1151-1167
19. Shengfu, W.: Spline Function and their Application, Northwestern Polytechnical University Press, 1989.
20. Khalil, H.: Nonlinear Systems, third edition. Upper Saddle River, NJ: Prentice-Hall, 2002.