Abstract
CMAC Neural Network is a popular choice for control applications. One of the main problems with CMAC is that the memory needed for the network grows exponentially with each addition of input variable. In this paper, we present a new CMAC architecture with more effective allocation of the available memory space. The proposed architecture employs hierarchical clustering to perform adaptive quantization of the input space by capturing the degree of variation in the output target function to be learned. We showed through a car maneuvering control application that using this new architecture, the memory requirement can be reduced significantly compared with conventional CMAC while maintaining the desired performance quality.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Albus, J.S.: A new approach to manipulator control: The cerebellar model articulation controller (CMAC). J. Dynamic Syst., Measurement, Contr., Trans. ASME, 220–227 (1975)
Yamamoto, T., Kaneda, M.: Intelligent controller using CMACs with self-organized structure and its application for a process system. IEICE Trans. Fundamentals E82-A, 856–860 (1999)
Commuri, S., Jagannathan, S., Lewis, F.L.: CMAC neural network control of robot manipulators. J. Robot Syst. 14, 465–482 (1997)
Wahab, A., Tan, E.C., Abut, H.: HCMAC amplitude spectral subtraction for noise cancellation. In: Intl. Conf. Neural Inform. Processing (2001)
Huang, K.L., Hsieh, S.C., Fu, H.C.: Cascade-CMAC neural network applications on the color scanner to printer calibration. In: Intl. Conf. Neural Networks, vol. 1, pp. 10–15 (1997)
Moody, J.: Fast-learning in multi-resolution hierarchies. In: Adv. Neural Infor. Processing Syst., vol. 14, pp. 29–38. Morgan Kauffman Publishers, San Francisco (1989)
Menozzi, A., Chow, M.: On the training of a multi-resolution CMAC neural network. In: 23rd. Intl. Conf. Ind. Electron. Contr. Instrum., vol. 3, pp. 1130–1135 (1997)
Yeh, M.F., Lu, H.C.: On-line adaptive quantization input space in cmac neural network. In: IEEE Intl. Conf. Syst., Man, Cybern., vol. 4 (2002)
Pasquier, M., Quek, C., Toh, M.: Fuzzylot: A self-organizing fuzzy neural rule-based pilot system for automated vehicle. Neural Networks 14, 1099–1112 (2001)
Ang, K.K., Quek, C.: An improved mcmac with momentum neighborhood and average trapezoidal output. IEEE Transactions on Systems, Man and Cybernetics Part B 30, 491–500 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Teddy, S.D., Lai, E.M.K. (2005). Hierarchical Clustering for Efficient Memory Allocation in CMAC Neural Network. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_74
Download citation
DOI: https://doi.org/10.1007/11550907_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)