Memory networks with asymmetric bonds
AIP Conference Proceedings 151 on Neural Networks for Computing
Asymmetric Hopfield-type networks: theory and applications
Neural Networks
Retrieval Properties of a Hopfield Model with Random Asymmetric Interactions
Neural Computation
Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements
IEEE Transactions on Computers
Stability of asymmetric Hopfield networks
IEEE Transactions on Neural Networks
Designing asymmetric Hopfield-type associative memory with higher order hamming stability
IEEE Transactions on Neural Networks
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A novel m energy functions method is adopted to analyze the retrieval property of continuous-time asymmetric Hopfield neural networks. Sufficient conditions for the local and global asymptotic stability of the network are proposed. Moreover, an efficient systematic procedure for designing asymmetric networks is proposed, and a given set of states can be assigned as locally asymptotically stable equilibrium points. Simulation examples show that the asymmetric network can act as an efficient associative memory, and it is almost free from spurious memory problem.