Tracking of non-stationary time-series using resource allocating RBF networks

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Using electricity load data and training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise and forgetting factors for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. We also find that a recently-proposed alternative novelty criterion, found to be more robust in stationary environments, does not fare so well in the non-stationary case due to the need for filter adaptability during training.

Experimental characterisation of the impact of neighbouring modulation-formats on 43Gb/s P-DPSK over SSMF and LEAF

We report an experimental characterisation examining the impact of differing 50GHz neighbouring modulation formats and bit rates on the performance of 43Gb/s P-DPSK over 1300km of SSMF and LEAF types. Performance is shown to be robust for hybrid P-DPSK and OOK systems. © VDE VERLAG GMBH.