Memory and learning of sequential patterns by nonmonotone neural networks
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Retrieval Property of Associative Memory Based on Inverse Function Delayed Neural Networks
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Temporal Sequences of Patterns with an Inverse Function Delayed Neural Network
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements
IEEE Transactions on Computers
Avoidance of the Permanent Oscillating State in the Inverse Function Delayed Neural Network
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
The hysteretic Hopfield neural network
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Further development of a network based on the Inverse Function Delayed (ID) model which can recall temporal sequences of patterns, is proposed. Additional advantage is taken of the negative resistance region of the ID model and its hysteretic properties by widening the negative resistance region and letting the output of the ID neuron be almost instant. Calling this neuron limit ID neuron, a model with limit ID neurons connected pairwise with conventional neurons enlarges the storage capacity and increases it even further by using a weightmatrix that is calculated to guarantee the storage after transforming the sequence of patterns into a linear separation problem. The network's tolerance, or the model's ability to recall a sequence, starting in a pattern with initial distortion is also investigated and by choosing a suitable value for the output delay of the conventional neuron, the distortion is gradually reduced and finally vanishes.