Title :
A theory for learning based on rigid bodies dynamics
Author_Institution :
Neural Networks & Adaptive Syst. Res. Group, Perugia Univ., Italy
fDate :
5/1/2002 12:00:00 AM
Abstract :
A new learning theory derived from the study of the dynamics of an abstract system of masses, moving in a multidimensional space under an external force field, is presented. The set of equations describing system´s dynamics may be directly interpreted as a learning algorithm for neural layers. Relevant properties of the proposed learning theory are discussed within the paper, along with results of computer simulations performed in order to assess its effectiveness in applied fields
Keywords :
computational complexity; learning (artificial intelligence); principal component analysis; signal processing; computer simulations; external force field; independent component analysis; learning theory; neural layers; orthonormal signal processing; principal component analysis; rigid bodies dynamics; unsupervised neural learning; Biomedical signal processing; Computer simulation; Direction of arrival estimation; Independent component analysis; Multidimensional signal processing; Multidimensional systems; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Signal processing algorithms;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2002.1000121
