Parameter Estimation in Nonlinear Systems Using Hopfield Neural Networks

A method using the Hopfield neural network is developed for estimating the parameters of a nonlinear system whose theoretical model is assumed to exist. A linearization procedure is presented, and the errors between the dynamics of the plant and its model are minimized through a cost function that is equated to the energy function of a Hopfield neural network. The minimization process yields the weights and biases of the neural network. Proof of convergence of the modeled parameters to their true values and boundedness of parameter estimates at each step are provided. Numerical results from a scalar time-varying problem and a complex nine-state aircraft problem are presented to demonstrate the potential of this method.