Tuning a fuzzy logic controller:an introduction
GZ Georgo Angelis · TU/e Research Portal · 1995
In this paper several tuning methods for Sugeno's fuzzy systems wiu be discussed.&t 4he first case the fuzzy controller is identified off-line based on training data.Following this approach, first the structure of the controller is identified by means o f a clustering algorithm.A Kohonen se4f-organizing neural network performs this task.TheE the parameters of the fuzzy controlier (ie.membership functions) are tuned by using a gradient descent algorithm.This approach shows analogies with training a Radial basis function neural network.In the second case the fuzzy controller will learn to control the system in an on-line situation.The controller parameters are adapted on a supervised manner by using a gradient descent method.To make the parameter adaptation possible we need to know the sens&ivity functions of the system or at least their sign.If this knowledge is availabk the specialised learning technique becomes possible.Otherwise we need a preceding learning stage, the identification of the system.It is easily shown that when a multilayer perceptrons neural network emulates the system the sensitivity functions of the system can be derived by a mechanism of back propagation (applying gradient descent) no more on the weights but on the input of the emulator.