Abstract
A fuzzy logic flight controller is mimicked by using a B-spline neural network model where the inputs are represented by B-spline functions. Choosing the order or distribution of the basis functions are important steps in the design of a B-spline neural model. A set of weights, which is optimised during the learning process, controls the mapping performed by the multi-dimensional basis functions. The weights can be seen as fuzzy singletons representing the consequent of the rules, the local control actions. The fuzzy controller can be seen as a B-spline interpolator whose working can be also interpreted by linguistic rules.
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