Simulated neurocontrol of an autogenous mill with evolutionary reinforcement learning

De Groenewald J.W.V. ; Aldrich C. ; Eksteen J.J. ; Conradie A.V.E. ; Coetzer L.P. (2007)

Conference Paper

In this investigation the development of nonlinear control system for an autogenous mill was considered. A symbiotic adaptive neuroevolution algorithm was used in conjunction with a dynamic multilayer perceptron model fitted to actual plant data to evolve neurocontrol systems. Simulation studies established the potential of the approach, which yielded satisfactory results, despite having had to learn from a model that covered part of the state space only. Copyright © 2007 IFAC.

Please refer to this item in SUNScholar by using the following persistent URL:
This item appears in the following collections: