|
Showing 1 - 10 of
10 matches in All Departments
What is combinatorial optimization? Traditionally, a problem is
considered to be c- binatorial if its set of feasible solutions is
both ?nite and discrete, i. e. , enumerable. For example, the
traveling salesman problem asks in what order a salesman should
visit the cities in his territory if he wants to minimize his total
mileage (see Sect. 2. 2. 2). The traveling salesman problem’s
feasible solutions - permutations of city labels - c- prise a
?nite, discrete set. By contrast, Differential Evolution was
originally designed to optimize functions de?ned on real spaces.
Unlike combinatorial problems, the set of feasible solutions for
real parameter optimization is continuous. Although Differential
Evolution operates internally with ?oating-point precision, it has
been applied with success to many numerical optimization problems
that have t- ditionally been classi?ed as combinatorial because
their feasible sets are discrete. For example, the knapsack
problem’s goal is to pack objects of differing weight and value
so that the knapsack’s total weight is less than a given maximum
and the value of the items inside is maximized (see Sect. 2. 2. 1).
The set of feasible solutions - vectors whose components are
nonnegative integers - is both numerical and discrete. To handle
such problems while retaining full precision, Differential
Evolution copies ?oating-point - lutions to a temporary vector
that, prior to being evaluated, is truncated to the nearest
feasible solution, e. g. , by rounding the temporary parameters to
the nearest nonnegative integer.
Presently, general-purpose optimization techniques such as
Simulated Annealing, and Genetic Algorithms, have become standard
optimization techniques. Concerted research efforts have been made
recently in order to invent novel optimization techniques for
solving real life problems, which have the attributes of memory
update and population-based search solutions. The book describes a
variety of these novel optimization techniques which in most cases
outperform the standard optimization techniques in many application
areas. New Optimization Techniques in Engineering reports
applications and results of the novel optimization techniques
considering a multitude of practical problems in the different
engineering disciplines presenting both the background of the
subject area and the techniques for solving the problems. "
Models form the basis of any decision. They are used in di?erent
context and for di?erent purposes: for identi?cation, prediction,
classi?cation, or control of complex systems. Modeling is done
theory-driven by logical-mathematical methods or data-driven based
on observational data of the system and some algorithm or software
for analyzing this data. Today, this approach is s- marized as Data
Mining. There are many Data Mining algorithms known like Arti?cial
Neural N- works, Bayesian Networks, Decision Trees, Support Vector
Machines. This book focuses on another method: the Group Method of
Data Handling. -
thoughthismethodologyhasnotyetbeenwellrecognizedintheinternational
science community asa verypowerfulmathematicalmodeling andknowledge
extraction technology, it has a long history. Developed in
1968bythe Ukrainianscientist A.G. Ivakhnenko it combines the
black-box approach and the connectionism of Arti?cial Neural
Networks with well-proven Statistical Learning methods and with
more behavior- justi?ed elements of inductive
self-organization.Over the past 40 years it has been improving and
evolving, ?rst by works in the ?eld of what was known in the U.S.A.
as Adaptive Learning Networks in the 1970s and 1980s and later by
signi? cantcontributions from scientists from Japan,China, Ukraine,
Germany. Many papers and books have been published on this modeling
technology, the vast majority of them in Ukrainian and Russian
language.
Models form the basis of any decision. They are used in di?erent
context and for di?erent purposes: for identi?cation, prediction,
classi?cation, or control of complex systems. Modeling is done
theory-driven by logical-mathematical methods or data-driven based
on observational data of the system and some algorithm or software
for analyzing this data. Today, this approach is s- marized as Data
Mining. There are many Data Mining algorithms known like Arti?cial
Neural N- works, Bayesian Networks, Decision Trees, Support Vector
Machines. This book focuses on another method: the Group Method of
Data Handling. -
thoughthismethodologyhasnotyetbeenwellrecognizedintheinternational
science community asa verypowerfulmathematicalmodeling andknowledge
extraction technology, it has a long history. Developed in
1968bythe Ukrainianscientist A.G. Ivakhnenko it combines the
black-box approach and the connectionism of Arti?cial Neural
Networks with well-proven Statistical Learning methods and with
more behavior- justi?ed elements of inductive
self-organization.Over the past 40 years it has been improving and
evolving, ?rst by works in the ?eld of what was known in the U.S.A.
as Adaptive Learning Networks in the 1970s and 1980s and later by
signi? cantcontributions from scientists from Japan,China, Ukraine,
Germany. Many papers and books have been published on this modeling
technology, the vast majority of them in Ukrainian and Russian
language.
What is combinatorial optimization? Traditionally, a problem is
considered to be c- binatorial if its set of feasible solutions is
both ?nite and discrete, i. e. , enumerable. For example, the
traveling salesman problem asks in what order a salesman should
visit the cities in his territory if he wants to minimize his total
mileage (see Sect. 2. 2. 2). The traveling salesman problem's
feasible solutions - permutations of city labels - c- prise a
?nite, discrete set. By contrast, Differential Evolution was
originally designed to optimize functions de?ned on real spaces.
Unlike combinatorial problems, the set of feasible solutions for
real parameter optimization is continuous. Although Differential
Evolution operates internally with ?oating-point precision, it has
been applied with success to many numerical optimization problems
that have t- ditionally been classi?ed as combinatorial because
their feasible sets are discrete. For example, the knapsack
problem's goal is to pack objects of differing weight and value so
that the knapsack's total weight is less than a given maximum and
the value of the items inside is maximized (see Sect. 2. 2. 1). The
set of feasible solutions - vectors whose components are
nonnegative integers - is both numerical and discrete. To handle
such problems while retaining full precision, Differential
Evolution copies ?oating-point - lutions to a temporary vector
that, prior to being evaluated, is truncated to the nearest
feasible solution, e. g. , by rounding the temporary parameters to
the nearest nonnegative integer.
Presently, general-purpose optimization techniques such as
Simulated Annealing, and Genetic Algorithms, have become standard
optimization techniques. Concerted research efforts have been made
recently in order to invent novel optimization techniques for
solving real life problems, which have the attributes of memory
update and population-based search solutions. The book describes a
variety of these novel optimization techniques which in most cases
outperform the standard optimization techniques in many application
areas. New Optimization Techniques in Engineering reports
applications and results of the novel optimization techniques
considering a multitude of practical problems in the different
engineering disciplines presenting both the background of the
subject area and the techniques for solving the problems. "
Computer-Aided Engineering Design with SolidWorks is designed for
students taking SolidWorks courses at college and university, and
also for engineering designers involved or interested in using
SolidWorks for real-life applications in manufacturing processes,
mechanical systems, and engineering analysis. The course material
is divided into two parts. Part I covers the principles of
SolidWorks, simple and advanced part modeling approaches, assembly
modeling, drawing, configurations/design tables, and surface
modeling. Part II covers the applications of SolidWorks in
manufacturing processes, mechanical systems, and engineering
analysis. The manufacturing processes applications include mold
design, sheet metal parts design, die design, and weldments. The
mechanical systems applications include: routing, piping and
tubing, gears, pulleys and chains, cams and springs, mechanism
design and analysis, threads and fasteners, hinges, and universal
joints. The sections on engineering analysis also include finite
element analysis.This textbook is unique because it is one of the
very few to thoroughly cover the applications of SolidWorks in
manufacturing processes, mechanical systems, and engineering
analysis, as presented in Part II.It is written using a hands-on
approach in which students can follow the steps described in each
chapter to: model and assemble parts, produce drawings, and create
applications on their own with little assistance from their
instructors during each teaching session or in the computer
laboratory. There are pictorial descriptions of the steps involved
in every stage of part modeling, assembly modeling, drawing
details, and applications presented in this textbook.Supplementary
Material(s)For Users (2 MB)
Group method of data handling (GMDH) is a typical inductive
modeling method built on the principles of self-organization. Since
its introduction, inductive modelling has been developed to support
complex systems in prediction, clusterization, system
identification, as well as data mining and knowledge extraction
technologies in social science, science, engineering, and
medicine.This is the first book to explore GMDH using MATLAB
(matrix laboratory) language. Readers will learn how to implement
GMDH in MATLAB as a method of dealing with big data analytics.
Error-free source codes in MATLAB have been included in
supplementary material (accessible online) to assist users in their
understanding in GMDH and to make it easy for users to further
develop variations of GMDH algorithms.
Applied Mechanics with SolidWorks aims to assist students,
designers, engineers, and professionals interested in using
SolidWorks to solve practical engineering mechanics problems. It
utilizes CAD software, SolidWorks-based, to teach applied
mechanics. SolidWorks here is presented as an alternative tool for
solving statics and dynamics problems in applied mechanics
courses.Readers can follow the steps described in each chapter to
model parts and analyze them. A significant number of pictorial
descriptions have been included to guide users through each stage,
making it easy for readers to work through the text on their
own.Instructional support videos showing the motions and results of
the dynamical systems being analyzed and SolidWorks files for all
problems solved are available to lecturers and instructors for free
download.
This senior undergraduate level textbook is written for Advanced
Manufacturing, Additive Manufacturing, as well as CAD/CAM courses.
Its goal is to assist students in colleges and universities,
designers, engineers, and professionals interested in using
SolidWorks as the design and 3D printing tool for emerging
manufacturing technology for practical applications. This textbook
will bring a new dimension to SolidWorks by introducing readers to
the role of SolidWorks in the relatively new manufacturing paradigm
shift, known as 3D-Printing which is based on Additive
Manufacturing (AM) technology. This new textbook: Features modeling
of complex parts and surfaces Provides a step-by-step tutorial type
approach with pictures showing how to model using SolidWorks Offers
a user-Friendly approach for the design of parts, assemblies, and
drawings, motion-analysis, and FEA topics Includes clarification of
connections between SolidWorks and 3D-Printing based on Additive
Manufacturing Discusses a clear presentation of Additive
Manufacturing for Designers using SolidWorks CAD software
"Introduction to SolidWorks: A Comprehensive Guide with
Applications in 3D Printing" is written using a hands-on approach
which includes a significant number of pictorial descriptions of
the steps that a student should follow to model parts, assemble
parts, and produce drawings.
|
|