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Neural Networks - Algorithms, Applications,and ... - Csbdu.in

Neural Networks - Algorithms, Applications,and ... - Csbdu.in

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52 Adal<strong>in</strong>e <strong>and</strong> Madal<strong>in</strong>e1.0J0.5 I:-1.5-1.0-0.5 OiS 1.0 1 ;50.3!0,2!0.1!-1.5-1.0-0.5 !-0.1f-0.2J0.75t0.5J0.250.5 1.0 1.5 0.5 1.0 1.5-0.5-0.750.20.1-1.5-1.0-0.5-0.10.5 1.0 1.5Figure 2.5-0.21The first three frequency-doma<strong>in</strong> components of a square waveare shown. Notice that the s<strong>in</strong>e waves each have differentmagnitudes, as <strong>in</strong>dicated by the coord<strong>in</strong>ates on the /axis, eventhough they are graphed to the same height.when we transmit a periodic square wave, we can observe the frequency-doma<strong>in</strong>effects <strong>in</strong> the time-doma<strong>in</strong> signal as overshoot, undershoot, <strong>and</strong> ripple.This example shows that the Fourier series can be a powerful tool <strong>in</strong> help<strong>in</strong>gus to underst<strong>and</strong> the frequency-doma<strong>in</strong> nature of any periodic signal, <strong>and</strong> topredict ahead of time what transmission effects we must consider as we designfilters for our signal-process<strong>in</strong>g applications.We can also apply Fourier analysis to aperiodic signals, by evaluat<strong>in</strong>g theFourier <strong>in</strong>tegral, which is given by

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