documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1
The BNL toolbox is a set of Matlab functions for defining and estimating the
parameters of a Bayesian network for discrete variables in which the conditional
probability tables are specified by logistic regression models. Logistic regression can be
used to incorporate restrictions on the conditional probabilities and to account for the
effect of covariates. Nominal variables are modeled with multinomial logistic regression,
whereas the category probabilities of ordered variables are modeled through a cumulative
or adjacent-categories response function. Variables can be observed, partially observed,
or hidden.
Java Media APIs: Cross-Platform Imaging, Media, and Visualization presents integrated Java media solutions that demonstrate the best practices for using this diverse collection. According to Sun MicroSystems, "This set of APIs supports the integration of audio and video clips, animated presentations, 2D fonts, graphics, and images, as well as speech input/output and 3D models." By presenting each API in the context of its appropriate use within an integrated media application, the authors both illustrate the potential of the APIs and offer the architectural guidance necessary to build compelling programs.
These are matlab and simulink files to model the membrane crystallization system, including the matlab file to get the optimation point of this system, and 3 simulink files, which are static model and 2 dynamic models. There has PID control and feed forward control for the dynamic models.
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
MATSNL is a package of MATLAB M-files for computing wireless sensor node lifetime/power budget and solving optimal node architecture choice problems. It is intended as an analysis and simulation tool for researchers and educators that are easy to use and modify. MATSNL is designed to give the rough power/ lifetime predictions based on node and application specifications while giving useful insight on platform design for the large node lifetime by providing side-by-side comparison across various platforms. The MATSNL code and manual can be found at the bottom of this page. A related list of publications describing the models used in MATSNL is posted on the ENALAB part of the 2 project at http://www.eng.yale.edu/enalab/aspire.htm
This chapter enables the reader to:
• Know the content and organization of this book, and how to use it to analyze
and model radar system performance
• Understand the concept of radar operation, the functions performed by radar,
and how radar may be used in various applications
• Understand the characteristics of functional radar models and how they are
used to analyze overall radar performance.
