| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 411156 | Neurocomputing | 2009 | 7 Pages |
Abstract
In this paper, coefficient sensitiveness of transiently chaotic neural network (TCNN) for maximum clique problem (MCP) is analyzed. By introducing a new definition of adaptivity into TCNN and utilizing a neuron filter, the domain of coefficient selection is enlarged to search globally optimal solution and near-optimal solution on MCP. Based on our analysis on the characteristic of TCNN, we propose a TCNN filter algorithm to shrink the network scale and improve solution quality. The algorithm effectively relaxes the coefficient sensitiveness and improves network ability to solve MCP. Simulations have been performed to verify the validity of our algorithm.
Keywords
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Gang Yang, Junyan Yi, Zhiqiang Zhang, Zheng Tang,