  Although the known maximum total generalized correntropy (MTGC) and
generalized maximum blakezisserman total correntropy (GMBZTC) algorithms can
maintain good performance under the errors-in-variables (EIV) model disrupted
by generalized Gaussian noise, their requirement for manual ad-justment of
parameters is excessive, greatly increasing the practical difficulty of use. To
solve this problem, the total arctangent based on logical distance metric
(TACLDM) algo-rithm is proposed by utilizing the advantage of few parameters in
logical distance metric (LDM) theory and the convergence behavior is improved
by the arctangent function. Compared with other competing algorithms, the
TACLDM algorithm not only has fewer parameters, but also has better robustness
to generalized Gaussian noise and significantly reduces the steady-state error.
Furthermore, the analysis of the algorithm in the generalized Gaussian noise
environment is analyzed in detail in this paper. Finally, computer simulations
demonstrate the outstanding performance of the TACLDM algorithm and the
rigorous theoretical deduction in this paper.
