Abstract
In this paper, a new improvement approach of the perfect recall rate of a block splitting type morphological associative memory (BMAM) is presented. The BMAM is one of MAMs without the kernel image, which is realized in more compact size as keeping the perfect recall rate as same as a normal MAM (without the kernel image). However, the MAM without kernel image has a problem that the perfect recall rate is inferior to a standard MAM (with the kernel image). Therefore, we try to improve the problem by a majority logic scheme and confirm the effectiveness of the proposed approach through autoassociation experiments of alphabet patterns compared to the traditional approaches in terms of the noise tolerance.
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References
Ritter, G.X., Sussner, P., Diaz-de-Leon, J.L.: Morphological associative memory. IEEE Trans. Neural Networks 9(2), 281–293 (1998)
Ida, T., Fuchida, T., Murashima, S.: The Effectiveness of Partical Connection on Morphological Associative Memory. IEICE J83, D-II(2), 863–867 (2000)
Davidson, J.L., Ritter, G.X.: A theory of morphological neural networks. In: Digital Optical Computing II, Proc. of SPIE, July 1990, vol. 1215, pp. 378–388 (1990)
Won, Y., Gader, P.D.: Morphological shared weight neural network for pattern classification and automatic target detection. In: Proc. of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, November 1995, vol. 4, pp. 2134–2138 (1995)
Won, Y., Gader, P.D., Coffield, P.: Morphological shared-weight networks with applications to automatic target recognition. IEEE Trans. Neural Networks 8(5), 1195–1203 (1997)
Davidson, J.L.: Simulated annealing and morphological neural networks. In: Image Algebra and Morphological Image Processing III, Proc. of SPIE, San Diego, CA, July 1992, vol. 1769, pp. 119–127 (1992)
Davidson, J.L., Hummer, F.: Morphology neural networks: an introduction with applications. IEEE Systems Signal Processing 12(2), 177–210 (1993)
Saeki, T., Miki, T.: Effectiveness of the Block Splitting Approach on Morphological Associative Memory without a Kernel Image. In: Proc. of WCCI (2006) (to be appeared)
Hattori, M., Fukui, A., Ito, H.: A Fast Method to Decide Kernel Patterns for Morphological Associative Memory. IEEJ Trans. EIS 123(10), 1830–1837 (2003)
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Saeki, T., Miki, T. (2006). Improvement of the Perfect Recall Rate of Block Splitting Type Morphological Associative Memory Using a Majority Logic Approach. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_40
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DOI: https://doi.org/10.1007/11893028_40
Publisher Name: Springer, Berlin, Heidelberg
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