In this paper, we show that a hierarchical Bayesian modelling approach to sequential learning leads to many interesting attributes such as regularisation and automatic relevance determination. We identify three inference levels within this hierarchy, namely model selection, parameter estimation and noise estimation. In environments where data arrives sequentially, techniques such as cross-valid...

Neural networks are applied to the system identification problems using adaptive algorithms for either parameter or functional estimation of dynamic systems. In this paper the neural networks' sensitivity to input values and connections' weights, is studied. The Reduction-Sigmoid-Amplification (RSA) neurons are introduced and four different models of neural network architecture are proposed and...

We consider the problem of efficient blind parameter estimation in terahertz (THz)-band ultra-massive multiple-input multiple-output (UM-MIMO) systems. UM-MIMO antenna arrays are crucial to overcoming distance THz communications. Following recent advancements transceiver design, such intrinsically compact and reconfigurable. propose dynamically tuning modulation types, operating frequencies, in...

In this paper, channel parameters are estimated and Adaptive Synchronization is done by using Kalman filter. The channel is assumed to be time varying in nature is modelled as a low-Pass tapped delay line filter. The Kalman filter technique is used to estimate the parameters of the channel with more accuracy. The parameters of the channel are time varying in nature. Channel estimation algorithm...