Abstract
This paper describes a new algorithm of self-organizing relationship (SOR) network for an efficient digital hardware implementation and also presents its digital hardware architecture. In SOR network, the weighted average of fuzzy inference takes heavy calculation cost. To cope with this problem, we propose a fast calculation algorithm for the weighted average using only active units. We also propose a new generating technique of membership function by representing its width on power-of-two, which suits well with the digital hardware bit-shift process. The proposed algorithm is implemented on FPGA with massively parallel architecture. The experimental result shows that the proposed SOR network architecture has a good approximation ability of nonlinear functions.
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
Yamakawa, T., Horio, K.: Self-organizing relationship (SOR) network. IEICE Trans. on Fundamentals E82-A(8), 1674–1678 (1999)
Koga, T., Horio, K., Yamakawa, T.: The Self-Organizing Relationship (SOR) Network Employing Fuzzy Inference Based Heuristic Evaluation. Neural Networks (in press)
Koga, T., Horio, K., Yamakawa, T.: Applications of brain-inspired SOR network to controller design and knowledge acquisition. In: Proc. of 5th Workshop on Self-Organizing Maps (WSOM 2005), pp. 687–694 (2005)
Horio, K., Haraguchi, T., Yamakawa, T.: An Intuitive Contrast Enhancement of an Image Data Employing the Self-Organizing Relationship (SOR). In: Proc. of Int. Joint Conf. on Neural Networks (IJCNN 1999), pp. 10–16 (1999)
Horio, K., Yamakawa, T.: Adaptive Self-Organizing Relationship Network and Its Application to Adaptive Control. In: Proc. of the 6th Int. Conf. on SoftComputing and Information/Intelligent Systems, pp. 299–304 (2000)
Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43, 59–69 (1982)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2001)
Tamukoh, H., Horio, K., Yamakawa, T.: Fast learning algorithms for self-organizing map employing rough comparison WTA and its digital hardware implementation. IEICE Trans. on Electronics E87-C(11), 1787–1794 (2004)
Chen, P.Y.: VLSI implementation for fuzzy membership-function generator. IEICE Trans. on Inf & Syst. E86-D(6), 1122–1125 (2003)
Jou, J.M., Chen, P.Y., Yang, S.F.: An adaptive fuzzy logic controller: its VLSI architecture and applications. IEEE Trans. on Very Large Scale Integr (VLSI) 8(1), 52–60 (2000)
Gabrielli, A., Gandolfi, E.: A fast digital fuzzy processor. IEEE Micro 19(1), 68–79 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tamukoh, H., Horio, K., Yamakawa, T. (2006). A Digital Hardware Architecture of Self-Organizing Relationship (SOR) Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_129
Download citation
DOI: https://doi.org/10.1007/11893295_129
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
eBook Packages: Computer ScienceComputer Science (R0)