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Tests were carried out for comparing the RNN and FNN based controllers with small
values of iteration number and population size. As it is our priority to achieve a good
evolutionary performance under a given condition, the parameters of the FNN (i.e.,
the number of hidden neurons and the range of its initial connection weights) were
thus tuned for the most suitable values. Although the parameters of the RNN were
set unfairly in the comparison, the RNN has provided an attractive performance while
significantly reduced the computation time. Aside from the self-connected neurons, the
RNN used in this paper includes the hidden and output layers that have other feedbacks
interacting each other. The RNN is thus considered to be suitable for such a sensitive
nonlinear system like the crane.
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