Minimax design of neural net controllers for highly uncertain plants
- 1 January 1994
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 5 (1) , 73-82
- https://doi.org/10.1109/72.265962
Abstract
This paper discusses the use of evolutionary programming (EP) for computer-aided design and testing of neural controllers applied to problems in which the system to be controlled is highly uncertain. Examples include closed-loop control of drug infusion and integrated control of HVAC/lighting/utility systems in large multi-use buildings. The method is described in detail and applied to a modified Cerebellar Model Arithmetic Computer (CMAC) neural network regulator for systems with unknown time delays. The design and testing problem is viewed as a game, in that the controller is chosen with a minimax criterion i.e., minimize the loss associated with its use on the worst possible plant. The technique permits analysis of neural strategies against a set of feasible plants. This yields both the best choice of control parameters and identification of that plant which is most difficult for the best controller to handle.Keywords
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