Made available in DSpace on 2014-12-17T14:55:03Z (GMT). No. of bitstreams: 1 OscarGF.pdf: 1901439 bytes, checksum: f8f1a37dca7a69d726f7a9453cbf0a98 (MD5) Previous issue date: 2004-03-05 / This work develops a robustness analysis with respect to the modeling errors, being applied to the strategies of indirect control using Artificial Neural Networks - ANN s, belong to the multilayer feedforward perceptron class with on-line training based on gradient method (backpropagation). The presented schemes are called Indirect Hybrid Control and Indirect Neural Control. They are presented two Robustness Theorems, being one for each proposed indirect control scheme, which allow the computation of the maximum steady-state control error that will occur due to the modeling error what is caused by the neural identifier, either for the closed loop configuration having a conventional controller - Indirect Hybrid Control, or for the closed loop configuration having a neural controller - Indirect Neural Control. Considering that the robustness analysis is restrict only to the steady-state plant behavior, this work also includes a stability analysis transcription that is suitable for multilayer perceptron class of ANN s trained with backpropagation algorithm, to assure the convergence and stability of the used neural systems. By other side, the boundness of the initial transient behavior is assured by the assumption that the plant is BIBO (Bounded Input, Bounded Output) stable. The Robustness Theorems were tested on the proposed indirect control strategies, while applied to regulation control of simulated examples using nonlinear plants, and its results are presented / Este trabalho utiliza as Redes Neurais Multicamadas - RNM s, totalmente com treinamento em tempo real (on-line), no desenvolvimento de duas estrat?gias de controle indireto. Os esquemas propostos denominam-se Controle H?brido Indireto e Controle Neural Indireto. Todo o treinamento dos neurodispositivos - o identificador da planta e o controlador, quando presentes na malha de controle indireto, ? realizado com um m?nimo de atraso computacional, de modo a contemplar o controle de plantas com pequenos per?odos de amostragem. S?o apresentados Teoremas de Estabilidade para garantia da converg?ncia dos dispositivos neurais, assim como foram feitas considera??es para adequar o m?todo de acelera??o da converg?ncia h-adaptativo utilizado ?s condi??es de estabilidade. Para cada esquema de controle indireto foi desenvolvido um teorema que permite calcular o m?ximo erro permanente (steady-state error) que poder? ocorrer em fun??o da toler?ncia previamente especificada para converg?ncia dos dispositivos neurais usados na malha de controle, desde que a estabilidade seja garantida. Estes teoremas foram denominados de Teoremas da Robustez e constituem a principal contribui??o deste trabalho. As condi??es de estabilidade e robustez foram testadas para as estrat?gias de Controle H?brido Indireto e de Controle Neural Indireto, sendo apresentados os resultados obtidos na simula??o computacional do controle de regula??o de plantas n?o-lineares, BIBO (Bounded Input, Bounded Output) est?veis

Reaction-diffusion models have been widely successful in explaining a large variety of patterning phenomena in biology ranging from embryonic development to cancer growth and angiogenesis. Firstly proposed by Alan Turing in 1952 and applied to a simple two-component system, reaction-diffusion models describe spontaneous spatial pattern formation, driven purely by interactions of the system components and their diffusion in space. Today, access to unprecedented amounts of quantitative biological data allows us to build and test biochemically accurate reaction-diffusion models of intracellular processes. However, any increase in model complexity increases the number of unknown parameters and thus the computational cost of model analysis. To efficiently characterize the behavior and robustness of models with many unknown parameters is, therefore, a key challenge in systems biology. Here, we propose a novel computational framework for efficient high-dimensional parameter space characterization of reaction-diffusion models. The method leverages the $L_p$-Adaptation algorithm, an adaptive-proposal statistical method for approximate high-dimensional design centering and robustness estimation. Our approach is based on an oracle function, which describes for each point in parameter space whether the corresponding model fulfills given specifications. We propose specific oracles to estimate four parameter-space characteristics: bistability, instability, capability of spontaneous pattern formation, and capability of pattern maintenance. We benchmark the method and demonstrate that it allows exploring the ability of a model to undergo pattern-forming instabilities and to quantify model robustness for model selection in polynomial time with dimensionality. We present an application of the framework to reconstituted membrane domains bearing the small GTPase Rab5 and propose molecular mechanisms that potentially drive pattern formation.