The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient STOCHASTIC mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application.
Electromagnetic scattering from the trees above a tilted rough ground plane generated by the STOCHASTIC Lidenmayer system is studied by Monte Carlo simulations in this paper.The scattering coefficients are calculated in three methods:coherent addition approximation,tree-independent scattering,and independent scattering.
Swarm intelligence algorithms are based on natural
behaviors. Particle swarm optimization (PSO) is a
STOCHASTIC search and optimization tool. Changes in the
PSO parameters, namely the inertia weight and the
cognitive and social acceleration constants, affect the
performance of the search process. This paper presents a
novel method to dynamically change the values of these
parameters during the search. Adaptive critic design
(ACD) has been applied for dynamically changing the
values of the PSO parameters.
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient STOCHASTIC mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
多目標(biāo)遺傳算法程序
to run Demo files, is to run SGALAB_demo_*.m
what s new:
1) Multiple-Objective GAs
VEGA
NSGA
NPGA
MOGA
2) More TSP mutation and Crossover methods
PMX
OX
CX
EAX
Boolmatrix
3) More selection methods
Truncation
tornament
STOCHASTIC
4) mutation methods
binary single point
int/real single point
5) encoding/decoding methods
binary
integer/real
messy
gray
DNA
permuation
to fix the plot bugs for 4001 , download this file and replace old files.
dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and smoothers, as well as useful classes such as common probability distributions and STOCHASTIC processes.
Measuring Frequency Content in
Signals
I this section we will study some non parametric methods for spectrum estimation
of a STOCHASTIC process. These methods are described in the literature.
All methods are based on the Periodogram which is defined for a sequence x[n]
with length N according to
Before delving into the details of orthogonal frequency division multiplexing (OFDM), relevant
background material must be presented first. The purpose of this chapter is to provide the necessary
building blocks for the development of OFDM principles. Included in this chapter are reviews of STOCHASTIC
and random process, discrete-time signals and systems, and the Discrete Fourier Transform (DFT). Tooled
with the necessary mathematical foundation, we proceed with an overview of digital communication
systems and OFDM communication systems. We conclude the chapter with summaries of the OFDM
wireless LAN standards currently in existence and a high-level comparison of single carrier systems versus
OFDM.
Performance analysis belongs to the domain of applied mathematics. The
major domain of application in this book concerns telecommunications sys-
tems and networks. We will mainly use STOCHASTIC analysis and probability
theory to address problems in the performance evaluation of telecommuni-
cations systems and networks. The first chapter will provide a motivation
and a statement of several problems.
