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A new technique for the minimization of customary cost functions for all-pole modeling of power spectral densities is presented. In the literature, optimizations are usually based on unnormalized autoregressive (AR) coefficients. In contrast, the proposed method is centered on a two-step descent using normalized AR coefficients on the one hand and the residual power on the other hand. For each cost...
In this paper, we study the circles packing problem which consists of packing a set of equal or unequal circles of known radii into a larger containing circle without overlapping. The energy landscape paving (ELP) method is a class of heuristic global optimization algorithm. By combining the ELP method with the gradient descent (GD) procedure, an improved energy landscape paving (ELP+) algorithm for...
This paper presents a practical approach for a nonlinear model predictive control scheme with collision avoidance which is implemented on a mobile robot with two differential wheels. In model predictive control, also called receding horizon control, cost function is formulated to minimize tracking error. The optimal control input is solving a discrete nonlinear optimization problem over a pre-described...
On-line decision making often involves query processing over time-varying data which arrives in the form of data streams from distributed locations. In such environments typically, a user application is interested in the value of some function defined over the data items. For example, the traffic management system can make control decisions based on the observed traffic at major intersections; stock...
The Broyden-Fletcher-Goldfarh-Shanno (BFGS) optimization algorithm usually used for nonlinear least squares is presented and is combined with the modified back propagation algorithm yielding a new fast training multilayer perceptron (MLP) algorithm (BFGS/AG). The approaches presented in the paper consist of three steps: (1) modification on standard back propagation algorithm by introducing "gain...
In this paper, we propose a support vector machine (SVM) meta-parameter optimization method which uses sequential number theoretic optimization (SNTO) and gradient information for better optimization performance. SNTO is a new global optimization approach whose foundation is numeric and statistic theory. This method has less computation time than genetic algorithm (GA) based and grid search based...
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