Neurocontrol

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.