Optimal Design of Hammerstein Cubic Spline Filter for Nonlinear System Modeling Based on Snake Optimizer

This article develops a new class of Hammerstein adaptive filters that contain a memoryless nonlinear system followed by a linear adaptive filter, where the nonlinear system comprises an adjustable look-up table and a spline interpolator. This article's first effort has been to employ the metaheuristic algorithm (MHA) to the Hammerstein spline adaptive filter (HSAF), where it concurrently updates the weights of spline control points and linear filter based on the estimation problem. A novel MHA called snake optimizer algorithm (SOA) is used to enhance the assurance of convergence, estimated parameter accuracy, and steady-state results. The presented experimental results indorse that the proposed SOA-based HSAF (SOA–HSAF) design exhibits more robust performance in dealing with higher degree nonlinear systems under the Gaussian and non-Gaussian circumstances compared to contemporary design methods like classical, some standard MHAs, and other researchers reported techniques. The achieved simulation results are validated using a TMS320C6713 digital signal processor.