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» Law Discovery using Neural Networks
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IJCAI
1997
13 years 6 months ago
Law Discovery using Neural Networks
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...
Kazumi Saito, Ryohei Nakano
DIS
1999
Springer
13 years 9 months ago
Discovery of a Set of Nominally Conditioned Polynomials
: 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...
Ryohei Nakano, Kazumi Saito
TSMC
1998
135views more  TSMC 1998»
13 years 4 months ago
Universal stabilization using control Lyapunov functions, adaptive derivative feedback, and neural network approximators
— 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...
Elias B. Kosmatopoulos
PAKDD
2000
ACM
100views Data Mining» more  PAKDD 2000»
13 years 8 months ago
Discovery of Relevant Weights by Minimizing Cross-Validation Error
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...
Kazumi Saito, Ryohei Nakano
ICTAI
2009
IEEE
13 years 12 months ago
Probabilistic Neural Logic Network Learning: Taking Cues from Neuro-Cognitive Processes
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...
Henry Wai Kit Chia, Chew Lim Tan, Sam Yuan Sung