The results focus primarily on stability and robustness, which are studied in light of the presence of externally generated exogenous input signals. Obtained results answers the question of asymptotic stabilization and tracking of a desired trajectory in the presence of a dynamic exosystem.
The results confirmed the working theory of robust stabilization using output feedback techniques, borne out of differential-geometric observer design principles.
An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view.
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We show that our algorithm is much faster than Kasuba's algorithm, and by increasing the number of training samples, the difference in speed grows enormously.
The performances of the SFAM and the MLP (multilayer perceptron) are compared on three problems: the two benchmarks, and the Farsi optical character recognition (OCR) problem.