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: Hybrid neural networks, Luffing system of portal cranes, Optimal design, Portal cranes