Recurrent canonical piecewise linear network: theory and application

A recurrent canonical piecewise linear (RCPL) network is defined by combining the canonical piecewise linear function with the autoregressive moving average (ARMA) model such that an augmented input space is partitioned into regions where an ARMA model is used in each. Properties of the RCPL network are discussed. Particularly, it is shown that the RCPL function is a contractive mapping and is stable in the sense of bounded input and bounded output stability. By generalizing Donoho´s minimum entropy deconvolution approach to the nonlinear case, it is shown that the RCPL network can achieve blind equalization. The RCPL network is applied to both supervised and blind equalization and results are presented to show that it is computationally efficient and with a very simple structure, can deliver highly satisfactory performance