Minimax design of neural net controllers for highly uncertain plants Export

@article{SeSc94, 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}, author = {Sebald, A. V. and Schlenzig, J.}, booktitle = {Neural Networks, IEEE Transactions on}, citeulike-article-id = {2641426}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/72.265962}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=265962}, doi = {10.1109/72.265962}, journal = {Neural Networks, IEEE Transactions on}, keywords = {minimax, robust\_design}, number = {1}, pages = {73--82}, posted-at = {2008-04-08 12:44:28}, priority = {2}, title = {Minimax design of neural net controllers for highly uncertain plants}, url = {http://dx.doi.org/10.1109/72.265962}, volume = {5}, year = {1994} }

TY - JOUR ID - SeSc94 L3 - citeulike-article-id:2641426 N2 - 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 IS - 1 JF - Neural Networks, IEEE Transactions on EP - 82 TI - Minimax design of neural net controllers for highly uncertain plants VL - 5 T2 - Neural Networks, IEEE Transactions on SP - 73 KW - minimax KW - robust_design AU - Sebald, AV AU - Schlenzig, J PY - 1994/// UR - http://dx.doi.org/10.1109/72.265962 ER -