Optimization methods for brain-like intelligent control
Paul J. Werbos · 2002
This paper defines a more restricted class of designs, to be called "brain-like intelligent control". The paper explains the definition and concepts behind it, describes benefits in control engineering, emphasizing stability, mentions 4 groups who have implemented such designs, for the first time, since late 1993, and discusses the brain as a member of this class, one which suggests features to be sought in future research. These designs involve approximate dynamic programming-dynamic programming approximated in generic ways to make it affordable on large-scale nonlinear control problems. These designs are based on learning. They permit a neural net implementation-like the brain but do not require it. They include some but not all "reinforcement learning" or "adaptive critic" designs.