A Gaussian neural network implementation for control scheduling

Using neurons with Gaussian nonlinearities, a neural network is designed to implement a control law scheduler. For the implementation discussed, the neural network is supplied information about existing operating conditions and then responds by supplying control law parameter values to the controller. The neural network has two layers of weights, and the values of the weights and biases are based on given operating points for the scheduler. By designing the neural network´s generalization behavior, specifications for the interpolation between the given operating points are satisfied. The neural network implementation performs best when the operating points are equidistant and has some drawbacks when used to implement multiparameter schedulers