Carrier-phase synchronization can be approached in a
general manner by estimating the multiplicative distortion (MD) to which
a baseband received signal in an RF or coherent optical transmission
system is subjected. This paper presents a unified modeling and
estimation of the MD in finite-alphabet digital communication systems. A
simple form of MD is the camer phase exp GO) which has to be estimated
and compensated for in a coherent receiver. A more general case with
fading must, however, allow for amplitude as well as phase variations of
the MD.
We assume a state-variable model for the MD and generally obtain a
Nonlinear estimation problem with additional randomly-varying system
parameters such as received signal power, frequency offset, and Doppler
spread. An extended Kalman filter is then applied as a near-optimal
solution to the adaptive MD and channel parameter estimation problem.
Examples are given to show the use and some advantages of this scheme.
In this article, we present an overview of methods for sequential simulation from posterior distributions.
These methods are of particular interest in Bayesian filtering for discrete time dynamic models
that are typically Nonlinear and non-Gaussian. A general importance sampling framework is developed
that unifies many of the methods which have been proposed over the last few decades in several
different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin
particular how to incorporate local linearisation methods similar to those which have previously been
employed in the deterministic filtering literature these lead to very effective importance distributions.
Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of
the analytic structure present in some important classes of state-space models. In a final section we
develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
To estimate the input-output mapping with inputs x
％ and outputs y generated by the following Nonlinear,
％ nonstationary state space model:
％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)]
％ + 8cos(1.2t) + process noise
％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3
％ + time varying measurement noise
％ using a multi-layer perceptron (MLP) and both the EKF and
％ the hybrid importance-samping resampling (SIR) algorithm.
Support Vector Machine is small sample method based on statistic learning theory. It is a new method to deal with the highly Nonlinear classification and regression problems .It can better deal with the small sample, Nonlinear and
Traveling Salesman Problem (TSP) has been an interesting problem for a long
time in classical optimization techniques which are based on linear and Nonlinear
programming. TSP can be described as follows: Given a number of cities to visit
and their distances from all other cities know, an optimal travel route has to be
found so that each city is visited one and only once with the least possible distance
traveled. This is a simple problem with handful of cities but becomes complicated
as the number increases.
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
an analysis software with souce code for the time series with methods based on the theory of Nonlinear deterministic dynamical systems, or chaos theory.這套軟件源碼是根據(jù)H. Kantz and T. Schreiber, ``Nonlinear Time Series Analysis , Cambridge University Press, Cambridge (1997).
GNU Octave is a high-level language, primarily intended for numerical
computations. It provides a convenient command line interface for
solving linear and Nonlinear problems numerically.
非線性有限元程序，NONSAP is a general finite element program for the Nonlinear static and dynamic analysis of complex structures. The program is very flexible and was designed to be extended and modified by the user. In particular the program can easily be modified to use a different formulation of the equations of motions, different time integration operators and other additional options.
