Network Parameters and Ranges.

<p>N_L - number of layers, [N_CELL] - number of cells in each layer vector, μ₀, μ⁺, μ⁻ - Levenberg-Marquardt optimization learning parameters.</p

Regression analysis between the predicted and desired values calculated using feedback control algorithm.

<p>The feedback control algorithm of DeFronzo <i>et al</i>. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044587#pone.0044587-DeFronzo2" target="_blank">[5]</a> was used for performance comparison, with a sampling rate of 10 minutes interval.</p

Real time glucose pump controller block diagram.

<p>In each time slot in a real time experiment, an input vector <i>f(P_i,G_i)</i> is calculated where <i>P_i</i> is the pump setting and <i>G_i</i> is the blood glucose level of step i. The controller’s prediction output <i>P_i+1</i> is derived as the median of 50 predictions.</p

Regression analysis between the predicted and desired values of the ANN glucose pump controller.

<p>Performance results of the Test set simulation. <b>A</b>: ANN trained using Levenberg-Marquardt (LM) optimization algorithm, <b>B</b>: ANN trained using Gradient-Descent with momentum and adaptive learning rate algorithm.</p

Glucose pump controller design stage block diagram.

<p>The output of this stage is an ensemble of 50 sets of Artificial Neural Network (ANN) connection weights, created using the Test set of data and the best fit parameters vector.</p

Animals Characteristics.

<p>SD - Sprague Dawley, BW - body weight, BPG - average basal plasma glucose concentration, GIR - glucose infusion rate.</p

Optimized Parameters and Best Performance.

<p>μ₀, μ⁺, μ⁻ - Levenberg-Marquardt optimization learning parameters, RMSE - root mean square error, cc - correlation coefficient.</p

Schematic configuration of the experimental setup.

<p>The system consists of three infusion syringe pumps for [3-³H] glucose, insulin and variable glucose respectively. Arterial catheter is connected to the infusion pumps, and venous catheter is used for manual blood sampling. A closed-loop, computer controlled system is proposed for maintaining plasma glucose concentration within the desired level during HEGC.</p

Data Groups Characteristics.

<p>SD - Sprague Dawley, BW - body weight, BPG - average basal plasma glucose concentration.</p

Evaluation of the error in the prediction of glucose infusion rate over different levels of random noise.

<p>Random Gaussian density function noise with zero mean, and variance corresponding to signal to noise ratios (SNR) of 5 dB to 35 dB was added to the input data. The prediction error is expressed in mean ± SEM over 100 simulations.</p