On the stability of the travelling salesman problem algorithm of Hopfield and Tank
Biological Cybernetics
An Efficient Multivalued Hopfield Network for the Traveling Salesman Problem
Neural Processing Letters
Modelling competitive Hopfield networks for the maximum clique problem
Computers and Operations Research
Design and analysis of maximum Hopfield networks
IEEE Transactions on Neural Networks
Shortest common superstring problem with discrete neural networks
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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Detection of isomorphism among kinematic chains is essential in mechanical design, but difficult and computationally expensive. It has been shown that both traditional methods and previously presented neural networks still have a lot to be desired in aspects such as simplifying procedure of identification and adapting automatic computation. Therefore, a new algorithm based on a competitive Hopfield network is developed for automatic computation in the kinematic chain isomorphism problem. The neural approach provides directly interpretable solutions and does not demand tuning of parameters. We have tested the algorithm by solving problems reported in the recent mechanical literature. Simulation results show the effectiveness of the network that rapidly identifies isomorphic kinematic chains.