This paper proposes a new connectionist approach to numeric law discovery; i.e., neural networks (law-candidates) are trained by using a newly invented second-order learning algor...
: This paper shows that a connectionist law discovery method called RF5X can discover a law in the form of a set of nominally conditioned polynomials, from data containing both nom...
— In this paper, the problem of stabilization of unknown nonlinear dynamical systems is considered. An adaptive feedback law is constructed that is based on the switching adaptiv...
In order to discover relevant weights of neural networks, this paper proposes a novel method to learn a distinct squared penalty factor for each weight as a minimization problem ov...
This paper describes an attempt to devise a knowledge discovery model that is inspired from the two theoretical frameworks of selectionism and constructivism in human cognitive le...