Abstract
This paper introduces the DPCNN (Delay Pulse Coupled Neural Network) based on the PCNN and uses the DPCNN to find the shortest path. Cauflield and Kinser introduced the PCNN method to solve the maze[1] and although their method also can be used to find the shortest path, a large quantity of neurons are needed. However, the approach proposed in this paper needed very fewer neurons than proposed by Cauflield and Kinser. Meanwhile, due to the parallel pulse transmission characteristic of the DPCNN, our approach can find the shortest path quickly. The computational complexity of our approach is only related to the length of the shortest path, and independent to the weighted graph complexity and the number of existed paths in the graph.
This work was supported by China Postdoctoral Science Foundation (No.2003034282) and National Natural Science Foundation of China (No.60171036 and No.30370392).
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
Caulfield, H.J., Kinser, J.M.: Finding Shortest Path in the Shortest Time Using PCNN’s. IEEE Trans. on Neural Networks 10, 604–606 (1999)
Ephremides, A., Verdu, S.: Control and Optimization Methods in Communication Network Problems. IEEE Trans. on Auto. Contr. 34, 930–942 (1989)
Ricca, F., Tonella, P.: Understanding and Restructuring Web Sites with ReWeb. IEEE Multimedia 8, 40–51 (2001)
Tenenbaum, J.B., Silva, V., de Langford, J.C.: A Global Geometric Framework for Nonlineaonality Reduction. Science 290, 2319–2323 (2000)
Yan, H.B., Liu, Y.C.: A New Algorithm for Finding Shortcut in a City’s Road Net Based on GIS Technology. Chinese J. Computers 23, 210–215 (2000)
Eckhorn, R., Reitboeck, H.J., Arndt, M., et al.: Feature Linking via Synchronization among Distributed Assemblies: Simulation of Results from Cat Cortex. Neural Computation 2, 293–307 (1990)
Johnson, J.L., Ritter, D.: Observation of Periodic Waves in a Pulse-coupled Neural Network. Opt. Lett. 18, 1253–1255 (1993)
Johnson, J.L., Padgett, M.L.: PCNN Models and Applications. IEEE Trans. on Neural Networks 10, 480–498 (1999)
Gu, X.D., Wang, H.M., Yu, D.H.: Binary Image Restoration Using Pulse Coupled Neural Network. In: The 8th International Conference on Neural Information Processing, Shanghai, China, pp. 922–927 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gu, X., Zhang, L., Yu, D. (2004). Delay PCNN and Its Application for Optimization. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_69
Download citation
DOI: https://doi.org/10.1007/978-3-540-28647-9_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
eBook Packages: Springer Book Archive