An Efficient Multivalued Hopfield Network for the Traveling Salesman Problem
Neural Processing Letters
Continuous-State Hopfield Dynamics Based on Implicit Numerical Methods
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
An Efficient Neural Network Algorithm for the p-Median Problem
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Single-row mapping and transformation of connected graphs
The Journal of Supercomputing
A neural model for the p-median problem
Computers and Operations Research
Improving Neural Networks for Mechanism Kinematic Chain Isomorphism Identification
Neural Processing Letters
Stochastic multivalued network for optimization: application to the graph Maxcut problem
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
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
A Hybrid of Particle Swarm Optimization and Hopfield Networks for Bipartite Subgraph Problems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Stochastic optimal competitive Hopfield network for partitional clustering
Expert Systems with Applications: An International Journal
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
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Improved stochastic competitive Hopfield network for polygonal approximation
Expert Systems with Applications: An International Journal
Parallelism in binary hopfield networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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Since McCulloch and Pitts presented a simplified neuron model (1943), several neuron models have been proposed. Among them, the binary maximum neuron model was introduced by Takefuji et al. and successfully applied to some combinatorial optimization problems. Takefuji et al. also presented a proof for the local minimum convergence of the maximum neural network. In this paper we discuss this convergence analysis and show that this model does not guarantee the descent of a large class of energy functions. We also propose a new maximum neuron model, the optimal competitive Hopfield model (OCHOM), that always guarantees and maximizes the decrease of any Lyapunov energy function. Funabiki et al. (1997, 1998) applied the maximum neural network for the n-queens problem and showed that this model presented the best overall performance among the existing neural networks for this problem. Lee et al. (1992) applied the maximum neural network for the bipartite subgraph problem showing that the solution quality was superior to that of the best existing algorithm. However, simulation results in the n-queens problem and in the bipartite subgraph problem show that the OCHOM is much superior to the maximum neural network in terms of the solution quality and the computation time