Nonlinear H∞ control for continuous-time recurrent neural networks

In this paper we investigate the nonlinear H_∞ control problem for recurrent neural network models connected to a recurrent neural network controller. Conditions for dissipativity with finite L₂-gain are derived and expressed as matrix inequalities, based on a two-hidden layer recurrent neural network in standard plant form. The matrix inequalities are obtained from a storage function of quadratic form or quadratic form plus integral terms. Narendra´s dynamic backpropagation procedure for training on a set of specific reference inputs is modified with a dissipativity condition.