204 pages | 147 B/W Illus.
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.
1 Soft computing2 Neural networks 3 Neural networks in computational mechanics4 Inverse problems in engineering5 Autoprogressive algorithm and self-learning simulation6 Evolutionary models7 Implicit redundant representation in genetic algorithm8 Inverse problem of engineering design