A Stochastic Neural Model for Fast Identification of Spatiotemporal Sequences
Neural Processing Letters
Nonlinear Modeling of Dynamic Systems with the Self-Organizing Map
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Spatio-temporal memories for machine learning: a long-term memory organization
IEEE Transactions on Neural Networks
Fault Detection, Diagnosis and Prediction in Electrical Valves Using Self-Organizing Maps
Journal of Electronic Testing: Theory and Applications
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A self-organizing neural net for learning and recall of complex temporal sequences is developed and applied to robot trajectory planning. We consider trajectories with both repeated and shared states. Both cases give rise to ambiguities during reproduction of stored trajectories which are resolved via temporal context information. Feedforward weights encode spatial features of the input trajectories, while the temporal order is learned by lateral weights through delayed Hebbian learning. After training, the net model operates in an anticipative fashion by always recalling the successor of the current input state. Redundancy in sequence representation improves noise and fault robustness. The net uses memory resources efficiently by reusing neurons that have previously stored repeated/shared states. Simulations have been carried out to evaluate the performance of the network in terms of trajectory reproduction, convergence time and memory usage, tolerance to fault and noise, and sensitivity to trajectory sampling rate. The results show that the model is fast, accurate, and robust. Its performance is discussed in comparison with other neural-networks models