Square root cubature Kalman filter for stocchastic metabolic/ hemodynamic model identification
Boureghda Mohammed, Toufik Bouden · 2017
Based on clinical data collected using different brain imaging and recording techniques, brain researchers built mathematical models of the activity in the human brain. To test these models they simulate them by performing on those models a virtual brain experiment and compare the outputs from those with the real brain activity recordings. The models can be a basis for understanding what goes wrong in brain diseases and brain disorders and potentially help to create new drugs for these conditions. These models are often formulated in a continuous-discrete state space form. To fit these models to actual data, this require having suitable techniques that permits us to estimate both the hidden states and parameters of such models. The method proposed in this paper is a combination between the Square Root Cubature Kalman Filter (SCKF) and Maximum Likelihood Estimation (MLE). Numerical results obtained with simulated data are presented and discussed.