Soft Computing in Engineering

Book details

  • Hardcover: 220 pages
  • Format: pdf
  • Size: 12 MB
  • Publisher: CRC Press; 1 edition (9 May 2018)
  • Language: English
  • ISBN-10: 9781498745673
  • ISBN-13: 978-1498745673
  • ASIN: 1498745679
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.

From Introduction
The term soft computing refers to a class of computational methods that are inspired by how biological systems operate in nature. We can think of them as problem-solving computational methods employed in nature by the biological systems. Soft computing methods are also referred to as computational intelligence methods. Included in this class of computational methods are artificial neural networks, genetic algorithm, fuzzy logic, and swarm intelligence. Neural networks are roughly modeled after the structure and the operation of brains and nervous systems in humans and animals. Genetic algorithm is mod- eled after the natural Darwinian evolution, fuzzy logic uses the linguistic approaches to problem-solving, and swarm intelligence is based on the decentralized and self-organized behavior of systems such as ant colonies. In this book, we will mainly concentrate on neural networks and genetic algorithm.

About the Author
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).






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