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.
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.
Neurocontrol: Learning Control Systems Inspired by Neuronal Architectures and Human Problem Solving Strategies
214
Neurocontrol: Learning Control Systems Inspired by Neuronal Architectures and Human Problem Solving Strategies
214Paperback(1992)
Product Details
| ISBN-13: | 9783540550570 |
|---|---|
| Publisher: | Springer Berlin Heidelberg |
| Publication date: | 02/21/1992 |
| Series: | Lecture Notes in Control and Information Sciences , #172 |
| Edition description: | 1992 |
| Pages: | 214 |
| Product dimensions: | 6.69(w) x 9.61(h) x 0.02(d) |