The evolution of 'Boxes' to quantized inductive learning: a study in inductive learning

An inductive learning method is analyzed for use in on-line control. The controller has the benefit of being designed without a system model and is able to adapt itself to varying system parameters. Numerical experiments were performed with the Quantized Inductive Learning (QIL) algorithm, an extension of ‘Boxes’, on simple linear systems and a simulated simply supported aluminum beam. Concurrent with the simulations, a theoretical analysis of the learning mechanism was generated. The evaluation of several issues with the algorithm (performance indices, sampling periods, and level of quantizations) were studied to validate the theory. A comparison with state feedback was used to compare the effectiveness of this method with traditional model-based approaches. The results indicate the method learns a control function which moves the system from an arbitrary initial condition to equilibrium or rejects a sinusoidal disturbance. In both cases, the control is learned in absence of an a priori system model.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

1996

Author dc:creator

• Pascoe, James

• artificial intelligence
• Machine learning
• damage control

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

etd-12172008-063016

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/46268