Real time system modelling using locally recurrent neural networks

In this paper dynamic neural networks for system modelling are considered: architectural issues are presented but the paper focuses on learning algorithms that work real-time. A recent architecture called locally recurrent neural network is presented in its different versions and compared to traditional networks internally static but provided with external buffer and MLP with finite memory synapses. Simulations results show better modelling performance for locally recurrent networks and so an improved training algorithm is developed for them: causal backpropagation through time. Validation tests shows that the networks are modelling the underlying system and not just overfitting the data.