@mastersthesis {Eckhard:2012,
	title = {Ferramentas para melhoria da converg{\^e}ncia dos m{\'e}todos de identifica{\c c}{\~a}o por erro de predi{\c c}{\~a}o},
	year = {2012},
	note = {n/a},
	school = {Universidade Federal do Rio Grande do Sul},
	type = {phd},
	address = {Porto Alegre},
	abstract = {<p>The Prediction Error Method is related to a non-convex optimization problem. It is usual to apply iterative algorithms to solve this optimization problem. However, iterative algorithms can get stuck at a local minimum of the cost function or converge to the border of the searching space. An analysis of the cost function and sufficient conditions to ensure the convergence of the iterative algorithms to the global minimum are presented in this work. It is observed that this conditions depend on the spectrum of the input signal used in the experiment. This work presents tools to improve the convergence of the algorithms to the global minimum, which are based on the manipulation of the input spectrum.</p>
},
	url = {http://hdl.handle.net/10183/75872},
	author = {D. Eckhard}
}
