Modified gradient algorithm for total least square filtering

A neural approach for the parameter estimation of adaptive FIR filters for linear system identification is presented in the paper. It is based on a linear neuron with a modified gradient algorithm, capable of resolving the total least squares (TLS) problem present in this kind of estimation, where noisy errors affect not only the observation vector but also the data matrix. The learning rule is analyzed mathematically. The results of computer simulations are given to illustrate that the neural approach considerably outperforms the existing TLS methods when a larger learning factor is used or the signal-noise-ratio (SNR) is lower.