The Regional Conjugate Optimisation Approach: A Novel Method for Three Parameter Identification of Physiological Models

Gradient descent parameter identification methods are typically effective for objective surfaces that are well conditioned, but can result in excessive computational cost or failed convergence when this is not the case. This research presents a novel Regional Conjugate Optimisation (RCO) approach, for parameter identification in three dimensions. The method characterises the objective surface prior to the iterative process, which improves the ability of the method to correctly determine key features of the objective surface and exploit these characteristics during iteration. The RCO method is validated using a Monte-Carlo methodology on a series of contrived objective surfaces, and compared to the LevenbergMarquardt (LMQ) gradient descent method. The RCO method demonstrated faster convergence in 96% of the tested cases, demonstrating the potential of this method to result in faster convergence, higher accuracy, and lower computational cost for certain classes of problems.