Adaptive algorithms lead to adjust the system coefficients based on the measured data. This paper presents a dichotomous coordinate descent method to reduce the computational complexity and to improve the tracking ability based on the variable forgetting factor when there are a lot of changes in the system. Vedic mathematics is used to implement the multiplier and the divider in the VFF equatio...

New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined recurrent neural network (PRNN) are presented. A computationally efficient gradient descent (GD) learning algorithm, together with a novel extended recursive least squares (ERLS) learning algorithm, are proposed. Simulation studies based on three speech signals that have been made public and are ava...

Equalization plays an important role for the communication system receiver to correctly recover the symbol send by the transmitter, where the received signals may contain additive noise and intersymbol interference (ISI). Blind equalization is a technique of many equalization techniques at which the transmitted symbols over a communication channel can be recovered without the aid of training se...

The exponentially weighted recursive least-squares (RLS) has a long history as an algorithm to track timevarying parameters in signal processing and time series analysis. By reviewing the optimality conditions of RLS under a regression framework, possible sources of suboptimality of RLS for tracking time-varying parameters, especially when the parameters satisfy a state-space model, are identif...

Composite load model is developed for 1-24 hours ahead prediction of hourly electric loads. The load model is composed of three components : the nominal load, the type load and the residual load. The nominal load is modeled such that the Kalman filter can be used and the parameters of the model are adapted by the exponentially weighted recursive least squares method. The type load component is ...