Multi-start Stochastic Competitive Hopfield Neural Network for p-Median Problem
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Competitive Hopfield network combined with estimation of distribution for maximum diversity problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Cascaded and hierarchical neural networks for classifying surface images of marble slabs
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Expert Systems with Applications: An International Journal
Frequency assignment problem in satellite communications using differential evolution
Computers and Operations Research
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
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We propose a novel approach, i.e., a noisy chaotic neural network with variable thresholds (NCNN-VT), to solve the frequency assignment problem in satellite communications. The objective of this NP-complete optimization problem is to minimize cochannel interference between two satellite systems by rearranging frequency assignments. The NCNN-VT model consists N times M of noisy chaotic neurons for an N-carrier M-segment problem. The NCNN-VT facilitates the interference minimization by mapping the objective to variable thresholds (biases) of the neurons. The performance of the NCNN-VT is demonstrated by solving a set of benchmark problems and randomly generated test instances. The NCNN-VT achieves better solutions, i.e., smaller interference with much lower computation cost compared to existing algorithms.