Multi-layer multi-feature map architecture for situational analysis

Author

Jakubowicz, Oleg G.

Author_Institution

Dept. of Electr. & Comput. Eng., State Univ. of New York, Buffalo, NY, USA

fYear

1989

fDate

0-0 1989

Firstpage

23

Abstract

A neural network architecture is described that can recognize and later reconstruct spatially related groupings of various objects. The recognition and reconstruction properties are invariant under input patterns that are translated, distorted, noisy, incomplete, and rotated by approximately 30 degrees with respect to the training patterns. The system utilizes massive redundancy and localized coagulation of spatial information in a manner related to K. Fukushima´s Neocognitron for visual image recognition. This system is the first to use multilayered multitopologically ordered T. Kohonen (1984) feature maps in a Neocognitron-related architecture. The model is described, applications are discussed, and results exhibiting an example run are presented.<>

Keywords

computerised pattern recognition; neural nets; parallel architectures; Neocognitron; computerised pattern recognition; model; multilayer multifeature map; neural network architecture; redundancy; situational analysis; training patterns; visual image recognition; Neural networks; Parallel architectures; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1989. IJCNN., International Joint Conference on

Conference_Location

Washington, DC, USA

Type

conf

DOI

10.1109/IJCNN.1989.118673

Filename

118673