| Summary: | 碩士 === 國立交通大學 === 控制工程系 === 84 === In this thesis, we use a fuzzy neural network (FNN) system to
approximatelycompute the partial derivative of an unknown
function. Since the FNN is a universal approximator and is
differentiable when its membership functions areall
differentiable, we will use its partial derivatives to
substitute the partial derivatives of an unknown function.
From the proposed FNN structure, the derivative of any order
can be easily obtained by only changing its membership
functions. As we know, a slight modeling error may cause a large
sensitivity error. This error is reduced by canceling the
redundant rules which are negligible on the function value
but are important on the derivative. Furthermore, this proposed
method is also illustrated by solving an optimization
problem and obtaining the sensitivity of the unknown system on
adaptive control.
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