In this work, we show the development of an adaptive algorithm based on the Ln(cosh \varepsilon) as cost function applied upon the error, called Sigmoidal Algorithm (SA). That function generates a surface which yields fast convergence along with lower misadjustment. It is similar to the family of algorithms proposed by Walach and Widrow [1]. The later ones were shown to behave poorer than the LMS algorithm [2], when the noise was Gaussian. We study the SA algorithm convergence behavior and find equations for the misadjustment and the learning time. Results showed that the SA had a better performance than the LMS when the noise had a Gaussian distribution.
['Ewaldo Santana', 'Jose C. Principe', 'Allan Kardec Barros', 'Raimundo C. S. Freire']
An Adaptive Algorithm Based On The Sigmoidal Function