Stable training of computationally intelligent systems by usingvariable structure systems technique
Onder Efe, M.; Kaynak, O.; Wilamowski, B.M.
Industrial Electronics, IEEE Transactions on
Volume 47, Issue 2, Apr 2000 Page(s):487 - 496
Digital Object Identifier 10.1109/41.836365
Summary:This paper presents a novel training algorithm for computationally
intelligent architectures, whose outputs are differentiable with respect
to the adjustable design parameters. The algorithm combines the gradient
descent technique with the variable-structure-systems approach. The
combination is performed by expressing the conventional weight update
rule in continuous time and application of sliding-mode control method
to the gradient-based training procedure. The proposed combination
therefore exhibits a degree of robustness with respect to the unmodeled
multivariable internal dynamics of gradient descent and to the
environmental disturbances. With conventional training procedures, the
excitation of this dynamics during a training cycle can lead to
instability, which may be difficult to alleviate due to the
multidimensionality of the solution space and the ambiguities on the
free design parameters, such as learning rate or momentum coefficient.
This paper develops a heuristic that a computationally intelligent
system, which may be a neural network architecture or a fuzzy inference
mechanism, can be trained such that the adjustable parameter values are
forced to settle down (parameter stabilization) while minimizing an
appropriate cost function (cost optimization). The proposed approach is
applied to the control of a robotic arm in two different ways. In one, a
standard fuzzy system architecture is used, whereas in the second, the
arm is controlled by the use of a multilayer perceptron. In order to
demonstrate the robustness of the approach presented, a considerable
amount of observation noise and varying payload conditions are also
studied
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