Abstract
An integrated neural-fuzzy approach named NUFZY has been developed in order to model non-linear systems. A set of membership functions at the input layer is linked with a layer of rules, using pre-set parameters. The consequent part of the rules are combined to form the output value by some weighted sum. Very fast training of the weights is achieved by using the orthogonal least squares method, which also provides a method to efficiently remove redundant rules from the prototype rule base of NUFZY. A practical example involving tomato plant growth data is used to demonstrate the capability of NUFZY.
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