Neural least-squares design of higher order digital differentiators

The least-squares (LS) design of higher order digital differentiators can be formulated as to solve a system of linear equations. This is completed by solving a matrix inversion or by using some matrix properties to simplify the optimization. Here, a Hopfield neural network (NN) with closed-form evaluation for the related parameters is utilized to solve the mentioned set of linear equations in real-time. Simulation results are given to illustrate the excellent performance of the proposed neural-based method.