FCMDEMO displays a GUI window to let you try out various PARAMETERS
in fuzzy c-means clustering for 2-D data. You can choose the data set
and clustering number from the GUI buttons at right, and then click
"Start" to start the fuzzy clustering process.
Routine mampres: To obtain amplitude response from h(exp(jw)).
input PARAMETERS:
h :n dimensioned complex array. the frequency response is stored
in h(0) to h(n-1).
n :the dimension of h and amp.
fs :sampling frequency (Hz).
iamp:If iamp=0: The Amplitude Res. amp(k)=abs(h(k))
If iamp=1: The Amplitude Res. amp(k)=20.*alog10(abs(h(k))).
output PARAMETERS:
amp :n dimensioned real array. the amplitude-frequency response is
stored in amp(0) to amp(n-1).
Note:
this program will generate a data file "filename.dat" .
in chapter 2
Routine mar1psd: To compute the power spectum by AR-model PARAMETERS.
Input PARAMETERS:
ip : AR model order (integer)
ep : White noise variance of model input (real)
ts : Sample interval in seconds (real)
a : Complex array of AR PARAMETERS a(0) to a(ip)
Output PARAMETERS:
psdr : Real array of power spectral density values
psdi : Real work array
in chapter 12
Basic Test Concepts
DC PARAMETERS
AC PARAMETERS
Functional PARAMETERS
Device Characterization
Test Program Development
Analog Test Concepts
Test Using DSP Techniques in Testing
Noise Reduction Techniques in Testing
The standard optimum Kalman filter demands complete
knowledge of the system PARAMETERS, the input forcing functions, and
the noise statistics. Several adaptive methods have already been devised
to obtain the unknown information using the measurements and
the filter residuals.