Since heavily non-linear and/or very complex processes still pose a
problem for automatic control, they can often be handled easily by human
operators. The book describes re- sults from ten years of research on
learning control loops, which imitate these abilities. After discussing
the diffe- rencesto adaptive control some background on human informa-
tion processing and behaviour is put forward and some lear- ning control
loop structure related to these ideas is shown. The ability to learn is
due to memories, which are able to interpolate for multi-dimensional
input spaces between scat- tered output values. A neuronally and
mathematically inspi- red memory lay out-are compared and it is shown
that they learn much faster thanbackpropagation neural networks, which
can also be used. For the learning control loop diffe- rent
architectures are given. Their usefulness is demonstra- ted by
simulation and results from applications to real pi- lot plants. The
book should be of interest for control engi- neers as well as
researchers in neural net applications and/or artificial intelligence.
The usual mathematical back- ground of engineers is sufficient.