SELF-LEARNING FOR AUTONOMOUS SYSTEMS
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.
COMPUTERS & INDUSTRIAL ENGINEERING
(1993). SELF-LEARNING FOR AUTONOMOUS SYSTEMS. COMPUTERS & INDUSTRIAL ENGINEERING, 25(41278), 401-404.
Available at: http://aquila.usm.edu/fac_pubs/6662