A direct learning law for a class of auto-associative dynamic neural networks

This paper proposes a new learning technique fur a class of additive dynamic auto-associative neural networks. In the proposed technique, which is based on the Jurdjevic-Quinn stabilization method for control affine systems, the network synaptic weights are directly related to the network states. Asymptotic stability of the training law is assured and a region of attraction around each point attractor can be predefined. The proposed learning law is simpler than existing techniques and requires the solution of significantly fewer nonlinear differential equations. The proposed technique is compared with existing techniques by way of an example.