Konstantinos Goulianas

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Signals in various applications are often generated by linear combinations of quantized components. The analysis of data into such components is treated here as a matrix analysis problem. We first show that the component alphabet can always be normalized to the levels 0, ..., M-1, without loss of generality. Then we study certain conditions under which the(More)
The objective of this research is the numerical estimation of the roots of a complete 2 × 2 nonlinear algebraic system of polynomial equations using a feed forward back-propagation neural network. The main advantage of this approach is the simple solution of the system, by building a structure—including product units—that simulates exactly the nonlinear(More)
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