An analysis of the computation of the arithmetic mean using only single-precision fixed-point arithmetic is presented. This is done to ease the timing constraints common to many on-line applications. Others have introduced several averaging algorithms in floating-point arithmetic for use in inferential statistics. In this paper, these algorithms are evaluated with respect to their feasibility as fixed-point methods in the context of real-time analysis. Modifications of these algorithms are presented, and previously unpublished ones are introduced in the interest of avoiding overflow (necessary) and minimizing truncation errors (highly desirable). All algorithms presented are tested for speed and accuracy on several sets of data, including their own “worst case.” The applicability of each algorithm is discussed with respect to some of the basic functions that real-time programs must perform.
The full text of this article is premium content
No entries found
Log in to Read the Full Article
Please select one of the options below for access to premium content and features.
Create a Web Account
If you are already an ACM member, Communications subscriber, or Digital Library subscriber, please set up a web account to access premium content on this site.
Join the ACM
Become a member to take full advantage of ACM's outstanding computing information resources, networking opportunities, and other benefits.
Subscribe to Communications of the ACM Magazine
Get full access to 50+ years of CACM content and receive the print version of the magazine monthly.
Purchase the Article
Non-members can purchase this article or a copy of the magazine in which it appears.