On the stability of the travelling salesman problem algorithm of Hopfield and Tank
Biological Cybernetics
Computation at the edge of chaos: phase transitions and emergent computation
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
Neuron-synapse IC chip-set for large-scale chaotic neural networks
IEEE Transactions on Neural Networks
2008 Special Issue: Threshold control of chaotic neural network
Neural Networks
Optimal matching by the transiently chaotic neural network
Applied Soft Computing
Quadratic Assignment Problems for Chaotic Neural Networks with Dynamical Noise
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Control and synchronization of chaotic neurons under threshold activated coupling
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
The entire range of Chaotic pattern recognition properties possessed by the Adachi neural network
Intelligent Decision Technologies
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We construct a mixed analog/digital chaotic neuro-computer prototype system for quadratic assignment problems (QAPs). The QAP is one of the difficult NP-hard problems, and includes several real-world applications. Chaotic neural networks have been used to solve combinatorial optimization problems through chaotic search dynamics, which efficiently searches optimal or near optimal solutions. However, preliminary experiments have shown that, although it obtained good feasible solutions, the Hopfield-type chaotic neuro-computer hardware system could not obtain the optimal solution of the QAP. Therefore, in the present study, we improve the system performance by adopting a solution construction method, which constructs a feasible solution using the analog internal state values of the chaotic neurons at each iteration. In order to include the construction method into our hardware, we install a multi-channel analog-to-digital conversion system to observe the internal states of the chaotic neurons. We show experimentally that a great improvement in the system performance over the original Hopfield-type chaotic neuro-computer is obtained. That is, we obtain the optimal solution for the size-10 QAP in less than 1000 iterations. In addition, we propose a guideline for parameter tuning of the chaotic neuro-computer system according to the observation of the internal states of several chaotic neurons in the network. network.