Stable dynamic backpropagation using constrained learning rate algorithm

An equilibrium point learning problem in discrete-time dynamic neural networks is studied in this paper using stable dynamic propagation with constrained learning rate algorithm. The new learning scheme provides an adaptive updating process of the synaptic weights of the network, so that the target pattern is stored at a stable equilibrium point. The applicability of the approach presented is illustrated through a binary pattern storage example.