Open AccessBook
Computational intelligence PC tools
TLDR
This book takes a hands-on, desktop-applications approach to the topic of computational intelligence, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book.Abstract:
Computational intelligence is an emerging field in computer science which combines fuzzy logic, neural networks, and genetic algorithms for a flexible yet powerful approach to scientific computing. Because computational intelligence combines three interrelated, mathematically-based tools, it has a wide variety of applications, from engineering and process control to experts systems. This book takes a hands-on, desktop-applications approach to the topic, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book. Features: * Concise introduction to the concepts of fuzzy logic, neural networks, and genetic algorithms, and how they relate to one another within the context of computational intelligence. * Computational intellignece applications, including self-organizing feature maps, fuzzy calculator, evolutionary programming, and fuzzy neural networks. * Detailed case studies from engineering (F-16 flight system), systems control (mass transit scheduling), and medicine (appendicitis diagnosis). * Windows floppy disk with both source code and executable, self-contained programs for desktop implementation of all of the book's applications.read more
Citations
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Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Proceedings ArticleDOI
A discrete binary version of the particle swarm algorithm
TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.
Book ChapterDOI
Parameter Selection in Particle Swarm Optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.
Proceedings ArticleDOI
Comparing inertia weights and constriction factors in particle swarm optimization
Russell C. Eberhart,Yuhui Shi +1 more
TL;DR: It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension.