a collection of M-files to study concepts in the following areas of Fuzzy-Set-Theory: Fuzzy or Multivalued Logic, The Calculus of Fuzzy, Quantities, Approximate Reasoning, Possibility Theory, Fuzzy Control, Neuro-Fuzzy Systems.
FISMAT accommodates different arithmetic operators, fuzzification and defuzzification algorithm, implication relations, and different method of Approximate reasoning such as Compositional Rule of Inference (CRI) and Approximate Analogical Reasoning Scheme based on Similarity Measure.
This program demonstrates some function approximation capabilities of a Radial Basis Function Network.
The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to Approximate the function between the given "sample" points.
A series of .c and .m files which allow one to perform univariate and bivariate wavelet analysis of discrete time series. Noother wavelet package is necessary -- everything is contained in this archive. The C-code computes the DWT and maximal overlap DWT. MATLAB routines are then used to compute such quantities as the wavelet variance, covariance, correlation, cross-covariance and cross-correlation. Approximate confidence intervals are available for all quantities except the cross-covariance and cross-correlation.
A set of commands is provided. For a description of this example, please see http://www.eurandom.tue.nl/whitcher/software/.
The present paper deals with the problem of calculating mean delays in polling systems
with either exhaustive or gated service. We develop a mean value analysis (MVA) to
compute these delay figures. The merits of MVA are in its intrinsic simplicity and its
intuitively appealing derivation. As a consequence, MVA may be applied, both in an
exact and Approximate manner, to a large variety of models.
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an Approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
Computes BER v EbNo curve for convolutional encoding / soft decision
Viterbi decoding scheme assuming BPSK.
Brute force Monte Carlo approach is unsatisfactory (takes too long)
to find the BER curve.
The computation uses a quasi-analytic (QA) technique that relies on the
estimation (Approximate one) of the information-bits Weight Enumerating
Function (WEF) using
A simulation of the convolutional encoder. Once the WEF is estimated, the analytic formula for the BER is used.
As a consequence, more exact models of devices can
be retained for analysis rather than the Approximate models commonly introduced
for the sake of computational simplicity. A computer icon appears in the margin
with each introduction of MATLAB analysis.
As a consequence, more exact models of devices can
be retained for analysis rather than the Approximate models commonly introduced
for the sake of computational simplicity. A computer icon appears in the margin
with each introduction of MATLAB analysis.
