Gaussian neural networks for control function implementation

The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network using Gaussian nonlinearities and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information, and its use is illustrated in an implementation of a control law scheduler.