Human beings epitomize the concept of "intelligent control."
Despite its apparent computational advantage over humans, no machine
or computer has come close to achieving the level of sensor-based
control which humans are capable of. Thus, there is a clear need to
develop computational methods which can abstract human decision-making
processes based on sensory feedback. Neural networks offer one such
method with their ability to map complex nonlinear functions. In this
paper, we examine the potential of an efficient neural network
learning architecture to the problems of system identification and
control. The cascade two learning architecture dynamically adjusts the
size of the network as part of the learning process. As such, it
allows different units to have different activation functions,
resulting in faster learning, smoother approximations, and fewer
required hidden units. We use the methods discussed here towards
identifying human control strategy.
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