Neurocontrol and fuzzy logic: Connections and designs

Paul J. Werbos
<span title="">1992</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="" style="color: black;">International Journal of Approximate Reasoning</a> </i> &nbsp;
Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques most often use verbal information from experts. Ideally, the two sources of information should be combined. For example, one can learn rules in a hybrid fashion and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high throughput hardware, and
more &raquo; ... nks to neurophysiology. Neurocontrol-the use of ANNs to directly control motors, actuators, etc.-uses five generalized designs, related to control theory, that can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design trade-offs and future directions are discussed throughout. The final section mentions a few new ideas regarding reasoning, planning, and chunking, with biological parallels.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1016/0888-613x(92)90017-t</a> <a target="_blank" rel="external noopener" href="">fatcat:ebkzw6hoejeldhginfnvtxz2pi</a> </span>
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