Brown, M. and Harris, C.J.
A Nonlinear Adaptive Controller: A Comparison between Fuzzy Logic Control and Neurocontrol.
IMA Jnl. of Mathematical Control and Information, 8, (3), .
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This paper explores, from a surface-fitting viewpoint, two algorithms which are often applied in the field of intelligent control: fuzzy self-organising controllers and neural networks. Both methodologies adapt internal model parameters in response to the plant's input-output mapping. However, while the convergence of single layer neural networks has been studied in great detail, very few theorems have been proved about self-organizing fuzzy logic controllers. In this paper, it is shown that B-splines can provide a framework for choosing the shape of the fuzzy sets. Then the operators chosen to implement the underlying fuzzy logic are examined, showing that they can produce `smooth' control surfaces. It is now possible to make a direct comparison between fuzzy logic controllers and feedforward neural networks, demonstrating that, in a forward-chaining mode, storing the plant's behaviour in terms of weights or rule confidences is equivalent. Finally, three training rules for the self-organizing fuzzy controller are derived.
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