Minimax design of CMAC encoded neural network controllers using evolutionary programming

The authors describe the use of evolutionary programming for computer-aided design and testing of cerebellar model arithmetic computer (CMAC) encoded neural network regulators. The design and testing problem is viewed as a game in that the controller parameters are to be chosen with a minimax criterion, i.e. to minimize the loss associated with their use on the worst possible plant parameters. The technique permits analysis of neural strategies against a set of plants. This gives both the best choice of control parameters and identification of the plant configuration which is most difficult for the best controller to handle