Anti-periodic solutions for high-order Hopfield neural networks
Computers & Mathematics with Applications
A Study into the Improvement of Binary Hopfield Networks for Map Coloring
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
An Improvement to Ant Colony Optimization Heuristic
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Global exponential stability of impulsive high-order Hopfield type neural networks with delays
Computers & Mathematics with Applications
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid Hopfield network-genetic algorithm approach for the terminal assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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High-order neural networks can be considered as an expansion of Hopfield neural networks, and have stronger approximation property and faster convergence rate. However, in practice high order network is seldom to be used to solve combinatorial optimization problem. In this paper crossbar switch problem, which is an NP-complete problem, is used as an example to demonstrate how to use high order discrete Hopfield neural network to solve engineering optimization problems. The construction method of energy function and the neural computing algorithm are presented. It is also discussed the method how to speed the convergence and escape from local minima. Experimental results show that high order network has a quick convergence speed, and outperforms the traditional discrete Hopfield network.