in this paper, a new smooth second order sliding mode control is proposed. this algorithm is a modified form of super twisting algorithm. the super twisting guarantees the asymptotic stability, but the finite time stability of proposed method is proved with introducing a new particular lyapunov function. the proposed algorithm which is able to control nonlinear systems with matched structured u...

A new estimator, named S-LASSO, is proposed for the coefficient function of Function-on-Function linear regression model. The S-LASSO estimator shown to be able increase interpretability model, by better locating regions where zero, and smoothly estimate non-zero values function. sparsity ensured a \textit{functional LASSO penalty}, which pointwise shrinks toward zero function, while smoothness...

Since in the physical world, most dependencies are smooth (differentiable), traditionally, functions were used to approximate these dependencies. In particular, neural networks activation such as sigmoid function. However, successes of deep learning showed that many cases, non-smooth like $$\max (0,z)$$ work much better. this paper, we explain why approximating often better—even when approximat...