This paper introduces a new method of registering point sets. The registration error is directly minimized using general-purpose nonlinear optimization (the Levenberg-Marquardt algorithm). The surprising conclusion of the paper is that this technique is comparable in speed to the special-purpose ICP algorithm which is most commonly used for this task. Because the routine directly minimizes an e...

Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due...

In this thesis, a special class of Recurrent Neural Networks (RNN) is employed for system identification and predictive control of time dependent systems. Fundamental architectures and learning algorithms of RNNs are studied upon which a generalized architecture over a class of state-space represented networks is proposed and formulated. Levenberg-Marquardt (LM) learning algorithm is derived fo...

Two trust-region methods for systems of mixed nonlinear equalities, general inequalities and simple bounds are proposed. The first method is based on a Gauss-Newton model, the second one is based on a regularized Gauss-Newton model and results to be a Levenberg-Marquardt method. The globalization strategy uses affine scaling matrices arising in bound-constrained optimization. Global convergence...

We present a model-based approach to recover the rigid and non-rigid facial motion parameters in video sequences. Our face model is based on anatomically motivated muscle actuator controls to model the articulated non-rigid motion of a human face. The model is capable of generating a variety of facial expressions by using a small number of muscle actuator controls. We estimate rigid and non-rig...