This volume details some of the latest advances in spectral theory and nonlinear analysis through various cutting-edge theories on algebraic multiplicities, global bifurcation theory, non-linear Schrodinger equations, non-linear boundary value problems, large solutions, metasolutions, dynamical systems, and applications to spatial ecology.
The fifth edition of this classic book continues its excellence in teaching numerical analysis and techniques. Interesting and timely applications motivate an understanding of methods and analysis of results. Suitable for students with mathematics and engineering backgrounds, the breadth of topics (partial differential equations, systems of nonlinear equations, and matrix algebra), provide comprehensive and flexible coverage of all aspects of all numerical analysis.
Autodesk Robot™ Structural Analysis Professional software is a collaborative, versatile, and faster software application that can help you compete and win in the global economy. Purpose-built for BIM, Autodesk Robot Structural Analysis Professional calculates even your more complex models with powerful finite element auto-meshing, nonlinear algorithms, and a comprehensive collection of design codes to help you achieve results in minutes, not hours.
This book is a compilation of some selected articles devoted to the analysis and control
of vibrations. Vibrations are a phenomenon found in many engineering systems; their
harmful effects are translated into low performance, noise, energy misspend,
discomfort and system breakdown, among others. These are the reasons why, in the
last years, researchers have made great efforts in seeking ways to eliminate them
totally or partially.
Back in the days when I had a lot more energy and a lot less sense, I wrote
the first edition of this book. I had just finished writing Microwave Mixers,
and friends kept asking me, “Well, are you going to write another one?”
Sales of Mixers were brisk, and the feedback from readers was
encouraging, so it was easy to answer, “Sure, why not?” After a year of
painful labor, Nonlinear Microwave Circuits was born.
It is more than a century since Karl Pearson invented the concept of Principal
Component Analysis (PCA). Nowadays, it is a very useful tool in data analysis in
many fields. PCA is the technique of dimensionality reduction, which transforms
data in the high-dimensional space to space of lower dimensions. The advantages of
this subspace are numerous. First of all, the reduced dimension has the effect of
retaining the most of the useful information while reducing noise and other
undesirable artifacts. Secondly, the time and memory that used in data processing
This volume is devoted to the study of feedback control of so-calledlinear
plant/nonlinear instrumentation(LPNI) systems. Such systems appear naturally in
situations where the plant can be viewed as linear but the instrumentation, that
is, actuators and sensors, can not.
Nonlinearity, Bifurcation and Chaos - Theory and Application is an edited book focused on introducing both theoretical and application oriented approaches in science and engineering. It' contains 12 chapters, and is recommended for university teachers, scientists, researchers, engineers, as well as graduate and post-graduate students either working or interested in the field of nonlinearity, bifurcation and chaos.
Upon completion of this chapter you should understand: Calculating linear breakeven points; calculating nonlinear breakeven points; effect of changes in costs and revenue; strategies associated with capacity limits, expansion and profits; isocosts and breakeven between products;...
This lecture will teach you how to fit nonlinear functions by using bases functions and how to control model complexity. The goal is for you to: Learn how to derive ridge regression; understand the trade-off of fitting the data and regularizing it; Learn polynomial regression; understand that, if basis functions are given, the problem of learning the parameters is still linear; learn cross-validation; understand model complexity and generalization.
The invariable motif for analog design is to explore the new circuit topologies, architectures and CAD technologies to overcome the design challenges coming from the new applications and new fabrication technologies. In this book, a new architecture for a SAR ADC is proposed to eliminate the process mismatches and minimize the errors.
The ANSYS program has many finite-element analysis capabilities, ranging from a simple, linear, static analysis
to a complex, nonlinear, transient dynamic analysis. The analysis guides in the ANSYS documentation
set describe specific procedures for performing analyses for different engineering disciplines.
The process for a typical ANSYS analysis involves three general tasks
6.002 Fall 2000
Discretize matter m1 m2 m3 m4 m5 KVL, KCL, i-v Composition rules Node method Superposition Thévenin, Norton LCA any circuit linear circuits
6.002 Fall 2000
Discretize value Digital abstraction Subcircuits for given “switch” setting are linear! So, all 5 methods (m1 – m5) can be
A =1 B =1
C RON RON
SR MOSFET Model
6.002 Fall 2000
Nonlinear Analysis Analytical method based on m1, m2, m3 Graphical method Introduction to incremental analysis
DWTs are constantly used to solve and treat more and more advanced problems. The
DWT algorithms were initially based on the compactly supported conjugate
quadrature filters (CQFs). However, a drawback in CQFs is due to the nonlinear phase
effects such as spatial dislocations in multi-scale analysis. This is avoided in
biorthogonal discrete wavelet transform (BDWT) algorithms, where the scaling and
wavelet filters are symmetric and linear phase. The biorthogonal filters are usually
constructed by a ladder-type network called lifting scheme.
In the preceding chapters, we introduced several different estimation principles and
algorithms for independent component analysis (ICA). In this chapter, we provide
an overview of these methods. First, we show that all these estimation principles
are intimately connected, and the main choices are between cumulant-based vs.
negentropy/likelihood-based estimation methods, and between one-unit vs. multiunit
methods. In other words, one must choose the nonlinearity and the decorrelation