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Abstract: Traditional portal cranes’ luffing system design process
generally includes employing case-based reasoning method for reasonable
initial parameters, and then optimizing them to get the optimal results.
But the approach is not desirable because it’s hard to decide which case
is the nearest and how to map the nearest case to the current problem,
also the initial parameters thereby are partial to a special case,
without general attributes of some type of cases. A hybrid neural
networks is presented based on optimization method for portal cranes’
luffing system design, which is simple in computation, and by which the
initial parameters obtained can lead to more desirable optimization
results.
Key words: Portal cranes Luffing system of portal cranes Hybrid
neural networks Optimal design
CLC No: U653.921
Received
20040513, received in revised form 20041108
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