Abstract
In this paper, discrete-time cellular neural networks with one-dimensional space invariant are designed to associative memories. The obtained results enable both heteroassociative and autoassociative memories to be synthesized by assuring the global asymptotic stability of the equilibrium point and the feeding data via external inputs rather than initial conditions. It is shown that criteria herein can ensure the designed input matrix to be obtained by using one-dimensional space-invariant cloning template. Finally, one specific example is included to demonstrate the applicability of the methodology.
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
Chua, L.O., Yang, L.: Cellular Neural Networks: Theory. IEEE Trans. Circuits Syst. 35, 1257–1272 (1988)
Brucoli, M., Carnimeo, L., Grassi, G.: Discrete-time Cellular Neural Networks for Associative Memories with Learning and Forgetting Capabilities. IEEE Trans. Circuits and Systems I 42, 396–399 (1995)
Liu, D., Lu, Z.: A New Synthesis Approach for Feedback Neural Networks Based on the Perceptron Training Algorithm. IEEE Trans. Neural Networks 8, 1468–1482 (1997)
Seiler, G., Schuler, A.J., Nossek, J.A.: Design of Robust Cellular Neural Networks. IEEE Trans. Circuits and Systems I 40, 358–364 (1993)
Liu, D., Michel, A.N.: Sparsely Interconnected Neural Networks for Associative Memories with Applications to Cellular Neural Networks. IEEE Trans. Circuits and Systems II 41, 295–307 (1994)
Grassi, G.: A New Approach to Design Cellular Neural Networks for Associative Memories. IEEE Trans. Circuits and Systems I 44, 835–838 (1997)
Grassi, G.: On Discrete-time Cellular Neural Networks for Associative Memories. IEEE Trans. Circuits and Systems I 48, 107–111 (2001)
Liao, X.X., Wang, J.: Algebraic Criteria for Global Exponential Stability of Cellular Neural Networks with Multiple Time Delays. IEEE Trans. Circuits and Systems I 50, 268–275 (2003)
Zeng, Z.G., Wang, J., Liao, X.X.: Global Exponential Stability of A General Class of Recurrent Neural Networks with Time-varying Delays. IEEE Trans. Circuits and Syst. I 50, 1353–1358 (2003)
Zeng, Z.G., Wang, J., Liao, X.X.: Stability Analysis of Delayed Cellular Neural Networks Described Using Cloning Templates. IEEE Trans. Circuits and Syst. I 51, 2313–2324 (2004)
Zeng, Z.G., Wang, J., Liao, X.X.: Global Asymptotic Stability and Global Exponential Stability of Neural Networks with Unbounded Time-varying Delays. IEEE Trans. on Circuits and Systems II, Express Briefs 52, 168–173 (2005)
Zeng, Z.G., Huang, D.S., Wang, Z.F.: Global Stability of A General Class of Discrete-time Recurrent Neural Networks. Neural Processing Letters 22, 33–47 (2005)
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
Zeng, Z., Wang, J. (2006). Associative Memories Based on Discrete-Time Cellular Neural Networks with One-Dimensional Space-Invariant Templates. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_121
Download citation
DOI: https://doi.org/10.1007/11759966_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
eBook Packages: Computer ScienceComputer Science (R0)