A new IIR-MLP learning algorithm for on-line signal processing

We propose a new learning algorithm for locally recurrent neural networks, called truncated recursive backpropagation which can be easily implemented on-line with good performance. Moreover it generalises the algorithm proposed by Waibel et al. (1989) for TDNN, and includes the Back and Tsoi (1991) algorithm as well as BPS and standard on-line backpropagation as particular cases. The proposed algorithm has a memory and computational complexity that can be adjusted by a careful choice of two parameters h and h' and so it is more flexible than a previous algorithm proposed by us. Although for the sake of brevity we present the new algorithm only for IIR-MLP networks, it can be applied also to any locally recurrent neural network. Some computer simulations of dynamical system identification tests, reported in literature, are also presented to assess the performance of the proposed algorithm applied to the IIR-MLP.