RMS = √(∑xi2/n)
>> a = [1 3 10 2 5 7 8 17 14]
a =
1 3 10 2 5 7 8 17 14
>> b = min(a)
b =
1
>> c = max(a)
c =
17
>> d = mean(a)
d =
7.4444
>> e = a.^2
e =
1 9 100 4 25 49 64 289 196
>> f = mean(e)
f =
81.8889
>> g = sqrt(f)
g =
9.0492
where
b = minimum
c = maximum
d = mean/average
e = square
f = mean-square
g = root-mean-square (RMS)
a =
1 3 10 2 5 7 8 17 14
>> b = min(a)
b =
1
>> c = max(a)
c =
17
>> d = mean(a)
d =
7.4444
>> e = a.^2
e =
1 9 100 4 25 49 64 289 196
>> f = mean(e)
f =
81.8889
>> g = sqrt(f)
g =
9.0492
where
b = minimum
c = maximum
d = mean/average
e = square
f = mean-square
g = root-mean-square (RMS)
The RMS value can also be worked out in a straight-forward command:
>> a_rms = sqrt(sum(a.^2)/length(a))
a_rms =
9.0492
Or use rms in the digital signal processing toolbox:
>> a_rms_toolbox = rms(a)
a_rms_toolbox =
9.0492
Reference:
Root mean square (Wikipedia)/平方平均數(維基百科)
rms: Root-mean-square level (MathWorks)
Matlab: Vector normalization to a new peak or RMS value (StudyEECC blog)
沒有留言:
張貼留言