Soft Computing in Engineering
1st Edition
Published on 11. May 2018
220 pages
978-1-4987-4568-0 (ISBN)
Description
Soft computing methods such as neural networks and genetic algorithms draw on the problem solving strategies of the natural world which differ fundamentally from the mathematically-based computing methods normally used in engineering. Human brains are highly effective computers with capabilities far beyond those of the most sophisticated electronic computers. The 'soft computing' methods they use can solve very difficult inverse problems based on reduction in disorder.
This book outlines these methods and applies them to a range of difficult engineering problems, including applications in computational mechanics, earthquake engineering, and engineering design. Most of these are difficult inverse problems - especially in engineering design - and are treated in depth.
This book outlines these methods and applies them to a range of difficult engineering problems, including applications in computational mechanics, earthquake engineering, and engineering design. Most of these are difficult inverse problems - especially in engineering design - and are treated in depth.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
3 Tables, black and white; 147 Line drawings, black and white; 147 Illustrations, black and white
ISBN-13
978-1-4987-4568-0 (9781498745680)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Jamshid Ghaboussi
Soft Computing in Engineering
Book
09/2020
1st Edition
CRC Press
€72.50
Shipment within 15-20 days
Jamshid Ghaboussi
Soft Computing in Engineering
Book
05/2018
1st Edition
CRC Press
€131.00
Shipment within 15-20 days
Person
Jamshid Ghaboussi is Emeritus Professor in Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. He received his doctoral degree from University of California at Berkeley. He has over 40 years of teaching and research experience in computational mechanics and soft computing with applications in structural engineering, geo-mechanics and bio-medical engineering. He has published extensively in these areas and is the inventor in five patents, mainly in the application of soft computing and computational mechanics. He is the co-author of books Numerical Methods in Computational Mechanics (CRC Press) and Nonlinear Computational Solid Mechanics (CRC Press). In recent years he has been conducting research on complex systems and has co-authored a book on Understanding Systems: A Grand Challenge for 21st Century Engineering (World Scientific Publishing).
Content
1 Soft computing 2 Neural networks 3 Neural networks in computational mechanics 4 Inverse problems in engineering 5 Autoprogressive algorithm and self-learning simulation 6 Evolutionary models 7 Implicit redundant representation in genetic algorithm 8 Inverse problem of engineering design
