Neurocontrol: Learning Control Systems Inspired by Neuronal Architectures and Human Problem Solving Strategies (Lecture Notes in Control and Information Sciences, 172). This item is unavailable.Neurocontrol: Learning Control Systems Inspired by Neuronal Architectures and Human Problem Solving Strategies (Lecture Notes in Control and Information Sciences, 172)

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.

Processes which are heavily non-linear and/or very complex pose a problem for automatic control, yet they can often be handled easily by human operators. This book describes results from 10 years of research on learning control loops which imitate these human abilities. After discussing the contrast with adaptive control, the authors present some background on human information processing and behaviour. A neuronally-inspired memory layout and a mathematically inspired one are compared and it is shown that they learn much faster than back-propagation neural networks. Different architectures are given for the learning control loop. Their usefulness is demonstrated by simulation and results from applications to real pilot plants. The book should be of interest to control engineers as well as researchers in neural net applications and/or artificial intelligence.