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ABSTRACT: del Sur -Av. Alem 1253 -(8000) Bahía Blanca -Bs.As. Abstract— The Neural Network Cell Average -Order Statistics Constant False Alarm Rate (NNCAOS CFAR) detector is presented in this work. NNCAOS CFAR is a combined detection methodol-ogy which uses the effectiveness of neural networks to search for non homogeneities like clutter banks and multiple targets within the radar return. In ad-dition, the methodology proposed applies a conven-ient cell average (CA) or order statistics (OS) CFAR detector according to the context situation. Exhaus-tive analysis and comparisons show that NNCAOS CFAR has better performance than CA CFAR, OS CFAR and even CANN CFAR detectors (the latter, a previously proposed neural network based detector). Furthermore, it is verified that the new proposal presents a robust operation when maintaining a con-stant probability of false alarm under different ra-dar return situations.
05/2012;
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ABSTRACT: Av. Alem 1253 -(8000) Bahía Blanca -Bs.As. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CIC) Universidad Nacional del Sur -Av. Alem 1253 -(8000) Bahía Blanca -Bs.As. Abstract— In this work a new radar de-tection method is proposed, the Cell Av-erage Neural Network Constant false Alarm Rate (CANN CFAR), which can be used with Weibull distributed non homogeneous radar returns. This processor combines Maximum Likelihood estimation method with Neural Networks for the clutter parameter estimation, resolving homogeneity and determining clut-ter bank transition points and size. To char-acterize its performance, probability of detec-tion is evaluated using Monte Carlo simulations and compared to other efficient CFAR schemes. As a result, CANN CFAR detection has bet-ter performance than conventional CFAR pro-cessors, especially when detecting targets lo-cated near clutter heterogeneities. An addi-tional advantage of the proposed technique is its efficiency when determining clutter transi-tion points, bank size and threshold setting. This efficiency translates in lower computation time than other CFAR algorithms, mostly con-sidering real time processing.
02/2010;
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ABSTRACT: This correspondence addresses the problem of interval fuzzy model identification and its use in the case of the robust Wiener model. The method combines a fuzzy identification methodology with some ideas from linear programming theory. On a finite set of measured data, an optimality criterion which minimizes the maximum estimation error between the data and the proposed fuzzy model output is used. The min-max optimization problem can then be seen as a linear programming problem that is solved to estimate the parameters of the fuzzy model in each fuzzy domain. This results in lower and upper fuzzy models that define the confidence interval of the observed data. The model is called the interval fuzzy model and is used to approximate the static nonlinearity in the case of the Wiener model with uncertainties. The resulting model has the potential to be used in the areas of robust control and fault detection.
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 11/2005; · 3.08 Impact Factor
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04/2002;
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ABSTRACT: Orthogonal basis functions are a powerful tool for efficient
system representation. Except for the lattice realization, that is based
on the nice properties of Szego orthonormal polynomials, no other
adaptive IIR filter realization using orthonormal characteristics seems
to be extensively studied in the literature. However, many orthogonal
realizations for adaptive FIR filters, that are particularly suitable
for rational modeling, have been proposed in past years. In this paper,
we present some theoretical results related to the properties of a
generalized orthonormal realization when used for mean square output
error minimization in a system identification application. One result is
related to the low computational complexity of the updating gradient
algorithm when some properties of the orthonormal realization are used.
An additional result establishes conditions for the stationary points of
the proposed updating algorithm
Electronics, Circuits and Systems, 1998 IEEE International Conference on; 02/1998
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ABSTRACT: A strategy of design for the controller and for a dynamic filter which compensates for disturbances is presented. The proposed algorithm has the robustness criteria which allows the controller to perform well under model uncertainties. The stability and dynamic performance of the control system is studied in relation to model-plant parameter mismatch. The algorithm is specially suited for time-delay processes. Simulation and experimental evaluation of the method is included in the paper.
Control Theory and Applications, IEE Proceedings D [see also IEE Proceedings-Control Theory and Applications] 04/1988;
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ABSTRACT: In this work, a methodology for a controller design of multivariable linear time invariant systems is presented. The singular values of the return difference operator and the time constant for each control loop can be easily specified through a set of rational functions in the Laplace domain. The frequency response of an ideal controller is directly obtained, allowing the implementation of different controller structures (PI, PID etc.). First, the discussion is focused on some new results of the perturbation theory which constitute the basis of the proposed methodology. Later, a complete analysis of the resulting control scheme is presented. Its robustness with respect to stability, performance and interactions as well as the normality of the closed-loop system is demonstrated. Some implementation aspects for the formulation of the ideal controller matrix are discussed. Approximation of this ideal controller to different realizable structures is then discussed. Finally, a case study of the dual-composition control of two typical distillation columns currently found in the literature is presented.
Chemical Engineering Science.
