Title

Self-Learning for Autonomous Systems

Document Type

Article

Publication Date

9-1-1993

Department

Computing

Abstract

Learning is a key element in the strive for machine intelligence. Unsupervised learning is even more important for robots or autonomous systems that operate in remote environment away from human interactions, such as the case in the fully automated factory floor. To achieve unsupervised learning, a variety of models and techniques have been employed by investigators. In this paper some of the models, especially in the area of Neural Networks are compared and contrasted. Special consideration will be given self organizing maps (Kohonen Networks) [1,6]. A comparison of the Kohonen Networks and their biological counterpart is given. The introduction of these systems to increase the intelligence, and hence the autonomy of systems, is considered.

Publication Title

Computers and Industrial Engineering

Volume

25

Issue

41278

First Page

401

Last Page

404