We present a nonsmooth least squares reformulation of the complementarity problem and investigate its convergence properties. The global and local fast convergence results (under mild assumptions) are similar to some existing equation-based methods. In fact, our least squares formulation is obtained by modifying one of these equationbased methods (using the Fischer-Burmeister function) in such ...

This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than nec...

This study presents an analysis of a cerebral autoregulation (CA) model developed by Ursino and Lodi Ursino and Lodi (1997). We have used this model to analyze non-invasive measurements of cerebral blood flow velocity (CBFV) and arterial blood pressure obtained during postural change from sitting to standing for a healthy young subject. This paper includes a sensitivity analysis, ranking model ...

Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of β−-decay halflives of the class of nuclei that decay 100% by β− mode in their ground states. A fully-connected multilaye...

TECHNICAL REPORT – 2013, April Abstract The concept of delay/Doppler altimetry has been under study since the mid 90’s, aiming at reducing the measurement noise and increasing the along-track resolution in comparison with the conventional pulse limited altimetry. This paper introduces a new model for the mean backscattered power waveform acquired by a radar altimeter operating in SAR mode, as w...

The stability of power production in photovoltaics (PV) power plants is an important issue for large-scale gridconnected systems. This is because it affects the control and operation of the electrical grid. An efficient forecasting model is proposed in this paper to predict the next-day solar photovoltaic power using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms and r...

Inverse Artificial neural network (iANN) is applied to optimize the parameters for hydraulic turbine runner blades. This proposed method (iANN) inverts the artificial neural network (ANN) and uses an optimization method to find the optimum parameter value (or unknown parameter) for given required conditions. In order to do so, first an ANN, which consists of a feedforward network with one hidde...

The performance of sensor-less controlled induction motor drive depends on the accuracy of the estimated speed. Conventional estimation techniques being mathematically complex require more execution time resulting in poor dynamic response. The nonlinear mapping capability and powerful learning algorithms of neural network provides an alternative for on-line speed estimation. A structurally comp...