Title :
Robustness of attractor networks and an improved convex corner detector
Author :
Nachbar, P. ; Schuler, A.J. ; Füssl, T. ; Nossek, Josef A. ; Chua, Leon O.
Author_Institution :
Inst. of Network Theory & Circuit Design, Tech. Univ. of Munich, Germany
Abstract :
The authors point out that by defining several notions of robustness for an attractor network, it is possible to augment previous results about the AdaTron algorithm by explicit values for the robustness of the optimal weights. It is shown that the symmetry of a problem is reflected by the invariance of the optimal weights. This enables one to deduce that a convex corner detection, using a discrete-time cellular neural network (DTCNN), cannot be accomplished with just one clock cycle, and an improved convex corner detector is proposed
Keywords :
edge detection; invariance; neural nets; AdaTron algorithm; attractor networks; convex corner detector; discrete-time cellular neural network; image recognition; invariance; optimal weights; robustness; Cellular neural networks; Clocks; Constraint theory; Detectors; Educational institutions; Neurons; Quadratic programming; Reflection; Robustness; Stability;
Conference_Titel :
Cellular Neural Networks and their Applications, 1992. CNNA-92 Proceedings., Second International Workshop on
Conference_Location :
Munich
Print_ISBN :
0-7803-0875-1
DOI :
10.1109/CNNA.1992.274355