There are two sources of error that limit the
accuracy of DDAs: • Rounding/truncation errors due to the limited precision of the registers, • Approximation errors due to the chosen
numerical integration method. Both of these error sources are cumulative, due to the repeated addition nature of DDAs. Therefore longer problem time results in larger inaccuracy of the resulting solution. The effect of rounding/truncation errors can be reduced by using larger registers. However, as this reduces the scaling constant
K, it also increases problem time and therefore may not significantly improve accuracy and in
real time DDA based systems may be unacceptable. The effect of approximation errors can be reduced by using a more accurate numerical integration algorithm than rectangular integration (e.g., trapezoidal integration) in the DDA integrators. DDAs could be seen as a special form of
analog computers, being configured like analog computers with digital instead of analog computing elements. All computing elements perform integration not as a numerical representation but as a continuous accumulation of differential quantities in the form of impulses. Therefor this type of computer has both advantages and drawbacks via the digital implementation of the analog computing paradigm. These are primarily introduced by numerical instabilities caused by the choice of the integration method and the fact that high clocking frequencies and short integration steps are needed, which also leads to a higher energy demand. On the other hand complex functions and variables like in partial differential equations are easier to simulate on a DDA than on a classical differential analyzer or analog computer. ==Patents==