Neurocontrol : Learning Control Systems Inspired by Neuronal Architectures and Human Problem Solving Strategies 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