Seven Tuning Schemes for an Adaline Model To Predict Floor Pressures in a Subsonic Cavity Flow

Abstract

This paper presents a simple yet effective one-step-ahead predictor based on an adaptive linear element (ADALINE). Several tuning schemes are studied to see whether the obtained model is consistent. The process under investigation is a subsonic cavity flow system. The experimental data obtained from the system is post-processed to obtain a useful predictor. The contribution of the paper is to demonstrate that despite the spectral richness of the observed data, a simple model with various tuning schemes can help to a satisfactory extent. Seven algorithms are studied, including the least mean squares (LMS), recursive least squares (RLS), modified Kaczmarz's algorithm (MK), stochastic approximation algorithm (SA), gradient descent (GD), Levenberg-Marquardt optimization technique (LM) and sliding mode-based tuning (SM). The model and its properties are discussed comparatively.