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, and can solve difficult inverse problems based on reduction in disorder -- such as in computational mechanics, earthquake engineering, structural control and engineering design.
Inhaltsverzeichnis
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
