Chaotic Potts spin model for combinatorial optimization problems
Neural Networks
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Neural Networks in Telecommunications
Neural Networks in Telecommunications
Self-Organising Pattern Formation: Fruit Flies and Cell Phones
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Static and dynamic channel assignment using neural networks
IEEE Journal on Selected Areas in Communications
On chaotic simulated annealing
IEEE Transactions on Neural Networks
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This paper presents a self-organizing transient chaotic neural network to solve the channel assignment problem, one of NP-complete problems. The proposed neural network consists of two parts. The first part is the self-organizing evolution stage, which based on the mutual inhibition mechanisms of bristle differentiation and the problem's heuristic information. The second part is the transient chaotic neural network executing stage. A significant property of the TCNN model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing in order to escape the local minima. In the proposed neural network, the first part is used to improve the quality of the obtained solutions. The simulating results have shown that the self-organizing transient chaotic neural network improves greatly performance through solving the well-known benchmark problems, especially for the Sivarajan's and Kunz's benchmark problems, while the performance is comparable with existing algorithms.