From PICLIST@MITVMA.MIT.EDU Fri Nov 15 15:56:14 2002 Received: from cherry.ease.lsoft.com [209.119.0.109] by dpmail10.doteasy.com with ESMTP (SMTPD32-7.13) id A99E3760150; Fri, 15 Nov 2002 15:56:14 -0800 Received: from PEAR.EASE.LSOFT.COM (209.119.0.19) by cherry.ease.lsoft.com (LSMTP for Digital Unix v1.1b) with SMTP id <3.007DE145@cherry.ease.lsoft.com>; Fri, 15 Nov 2002 18:42:03 -0500 Received: from MITVMA.MIT.EDU by MITVMA.MIT.EDU (LISTSERV-TCP/IP release 1.8d) with spool id 1056 for PICLIST@MITVMA.MIT.EDU; Fri, 15 Nov 2002 18:41:54 -0500 Received: from MITVMA (NJE origin SMTP@MITVMA) by MITVMA.MIT.EDU (LMail V1.2d/1.8d) with BSMTP id 4304; Fri, 15 Nov 2002 18:39:56 -0500 Received: from smtp.comcast.net [24.153.64.2] by mitvma.mit.edu (IBM VM SMTP Level 320) via TCP with ESMTP ; Fri, 15 Nov 2002 18:39:55 EST X-Comment: mitvma.mit.edu: Mail was sent by smtp.comcast.net Received: from pcp01441904pcs.walngs01.pa.comcast.net (pcp01441904pcs.walngs01.pa.comcast.net [68.81.234.24]) by mtaout01.icomcast.net (iPlanet Messaging Server 5.1 HotFix 1.5 (built Sep 23 2002)) with SMTP id <0H5N00L4V5PAL4@mtaout01.icomcast.net> for PICLIST@MITVMA.MIT.EDU; Fri, 15 Nov 2002 18:39:10 -0500 (EST) MIME-version: 1.0 X-Mailer: Forte Agent 1.5/32.452 Content-type: text/plain; charset=us-ascii Content-transfer-encoding: 7BIT References: Message-ID: <3dd58233.5953346@smtp.comcast.net> Date: Fri, 15 Nov 2002 23:37:34 +0000 Reply-To: pic microcontroller discussion list Sender: pic microcontroller discussion list From: Dave Dilatush Subject: Re: [EE]:Pressure sensor confusion To: PICLIST@MITVMA.MIT.EDU In-Reply-To: X-RCPT-TO: Status: R X-UIDL: 277600749 X-Evolution-Source: pop://mailinglist%40farcite.net@mail.farcite.net/ X-Evolution: 000007b1-0000 Lawrence Lile wrote... >That's one way to deal with it. > >A typical problem for me is interpolating thermistor data. The thermistor >curve comes in chart form from the manufacturer, in very un-handy >increments. It's quite a complex curve, and I think curve fitting it >would be a long involved task (unless Excel does this automatically >somehow? ) Linear curve fitting would be a cinch. The only way I know >how to curve fit a nonlinear is, guess at the type of equation to use, >plot it next to your data, sum the squares of the differences, then run a >linear regression on the results and use a Tools:GoalSeek on the results >to approach the minimum, then guess again at the type of equation to use >until you like the fit.. It is a time-consuming process, usually. > >Is there a more efficient way? Least-squares curve fitting will give you a set of coefficients for an interpolation polynomial and is especially useful where you can take a lot of data points, but the data are noisy. Another technique for generating interpolating polynomial coefficients is Newton's Method of Divided Differences. This method will take (N) data points and give you the coefficients for a (N-1)th-order polynomial that passes precisely through the points. The computations for this technique are easy, and it works very well when you have a limited amount of data but the data are largely noise-free. One writeup of Newton's Method that looks like a good summary of the technique is at: http://classes.cecs.ucf.edu/egn3420/klee/Notes/Interpolation/Interp_NDD.pdf but you can probably get more from Google. Dave D. -- http://www.piclist.com hint: The PICList is archived three different ways. See http://www.piclist.com/#archives for details.