Title :
A learning rule for CAM storage of continuous periodic sequences
Abstract :
An analytic formula is used to set weights in recurrent analog networks with higher-order correlations to achieve the associative or content-addressable memory (CAM) storage of continuous pattern sequences as periodic trajectories. This learning rule allows programming of characteristics of the network vector field independently of the spatiotemporal patterns to be stored. Stability of sequences, basin geometry, and rates of convergence may be determined. A Lyapunov function in a special coordinate system governs the approach of initial conditions to the nearest stored trajectory
Keywords :
Lyapunov methods; content-addressable storage; learning systems; neural nets; Lyapunov function; basin geometry; content-addressable memory; continuous pattern sequences; continuous periodic sequences; higher-order correlations; normal form projection algorithm; periodic attractor; periodic trajectories; recurrent analog networks; spatiotemporal patterns;
Conference_Titel :
Neural Networks, 1990., 1990 IJCNN International Joint Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/IJCNN.1990.137888
