A new algorithm for finding the shortest paths using PCNNs

Hong Qu and Zhang Yi

Chaos, Solitons & Fractals, 2007, vol. 33, issue 4, 1220-1229

Abstract: Pulse coupled neural networks (PCNNs), based on the phenomena of synchronous pulse bursts in the animal visual cortex, are different from traditional artificial neural networks. Caulfield and Kinser have presented the idea of utilizing the autowave in PCNNs to find the solution of the maze problem. This paper which studies the performance of the autowave in PCNNs aims at applying it to optimization problems, such as the shortest path problem. A multi-output model of pulse coupled neural networks (MPCNNs) is studied. A new algorithm for finding the shortest path problem using MPCNNs is presented. Simulations are carried out to illustrate the performance of the proposed method.

Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:33:y:2007:i:4:p:1220-1229

DOI: 10.1016/j.chaos.2006.01.097

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