Title :
Concept learning: Hierarchical system
Author_Institution :
US Naval Air Eng. Center, Lakehurst, NJ, USA
Abstract :
A hierarchical learning system was designed and simulated. The principal investigative tool was a perception-driven, goal-oriented control system. The system utilizes a multilayered neural network with a backpropagation learning mechanism, a set of competitive networks for feature extraction, and a set of neuron layers for performing XOR, OR, and AND operations. The author examines (a) conceptual learning (CL), that is, generating a complete set of Horn clauses with subsequent generalization, and (b) quantitative learning (QL), that is, adjusting the strength of connections (synapses) between nodes in a neural network
Keywords :
hierarchical systems; learning systems; neural nets; AND; Horn clauses; OR; QL; XOR; backpropagation learning mechanism; conceptual learning; hierarchical learning system; multilayered neural network; neuron layers; quantitative learning; Backpropagation; Control systems; Feature extraction; Hierarchical systems; Lakes; Learning systems; Neural networks; Neurons; Research and development; Robot sensing systems;
Conference_Titel :
Intelligent Control, 1990. Proceedings., 5th IEEE International Symposium on
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-8186-2108-7
DOI :
10.1109/ISIC.1990.128494
