SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Hopfield neural networks: a survey
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
Recalling Temporal Sequences of Patterns Using Neurons with Hysteretic Property
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Optimal matching by the transiently chaotic neural network
Applied Soft Computing
Hysteretic HNN MAI-Cancellation for CDMA Signals in Multipath Fading Channels
Neural Processing Letters
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Precision control of magnetostrictive actuator using dynamic recurrent neural network with hysteron
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
| Hi-index | 0.00 |
A new neuron activation function based on a property found in physical systems-hysteresis-is proposed. We incorporate this neuron activation in a fully connected dynamical system to form the hysteretic Hopfield neural network (HHNN). We then present an analog implementation of this architecture and its associated dynamical equation and energy function. We proceed to prove Lyapunov stability for this new model, and then solve a combinatorial optimization problem (i.e., the N-queen problem) using this network. We demonstrate the advantages of hysteresis by showing increased frequency of convergence to a solution, when the parameters associated with the activation function are varied