Abstract
An interpretation of the new fuzzy reasoning model (NFRM) is developed. This interpretation makes the traditional fuzzy reasoning model (FRM) a special case under certain conditions. In addition, a neural network is constructed to represent the NFRM. The proposed neuro-new fuzzy reasoning model (NNFRM) optimizes the parameters of the NFRM by using the well-known backpropagation concept. The parameters to be optimized are those of the input membership functions, output membership function and relation matrix. The proposed NNFRM is used to predict future values of a chaotic time series, which is considered a benchmark problem. It is shown that the proposed NNFRM outperforms other modeling methods in prediction of this chaotic time series. The NNFRM used here has fewer adjustable parameters, than those used in other modeling techniques.
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